Mathematics Books

Book SynopsisSymbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such a data.Table of Contents1 Introduction 1 2 Symbolic Data: Basics 7 2.1 Individuals, Classes, Observations, and Descriptions 8 2.2 Types of Symbolic Data 9 2.2.1 Multi-valued or Lists of Categorical Data 9 2.2.2 Modal Multi-valued Data 10 2.2.3 Interval Data 12 2.2.4 Histogram Data 13 2.2.5 Other Types of Symbolic Data 14 2.3 How do Symbolic Data Arise? 17 2.4 Descriptive Statistics 24 2.4.1 Sample Means 25 2.4.2 Sample Variances 26 2.4.3 Sample Covariance and Correlation 28 2.4.4 Histograms 31 2.5 Other Issues 38 Exercises 39 Appendix 41 3 Dissimilarity, Similarity, and Distance Measures 47 3.1 Some General Basic Definitions 47 3.2 Distance Measures: List or Multi-valued Data 55 3.2.1 Join and Meet Operators for Multi-valued List Data 55 3.2.2 A Simple Multi-valued Distance 56 3.2.3 Gowda–Diday Dissimilarity 58 3.2.4 Ichino–Yaguchi Distance 60 3.3 Distance Measures: Interval Data 62 3.3.1 Join and Meet Operators for Interval Data 62 3.3.2 Hausdorff Distance 63 3.3.3 Gowda–Diday Dissimilarity 68 3.3.4 Ichino–Yaguchi Distance 73 3.3.5 de Carvalho Extensisons of Ichino–Yaguchi Distances 76 3.4 Other Measures 79 Exercises 79 Appendix 82 4 Dissimilarity, Similarity, and Distance Measures: Modal Data 83 4.1 Dissimilarity/Distance Measures: Modal Multi-valued List Data 83 4.1.1 Union and Intersection Operators for Modal Multi-valued List Data 84 4.1.2 A Simple Modal Multi-valued List Distance 85 4.1.3 Extended Multi-valued List Gowda–Diday Dissimilarity 87 4.1.4 Extended Multi-valued List Ichino–Yaguchi Dissimilarity 90 4.2 Dissimilarity/Distance Measures: Histogram Data 93 4.2.1 Transformation of Histograms 94 4.2.2 Union and Intersection Operators for Histograms 98 4.2.3 Descriptive Statistics for Unions and Intersections 101 4.2.4 Extended Gowda–Diday Dissimilarity 104 4.2.5 Extended Ichino–Yaguchi Distance 108 4.2.6 Extended de Carvalho Distances 112 4.2.7 Cumulative Density Function Dissimilarities 115 4.2.8 Mallows’ Distance 117 Exercises 118 5 General Clustering Techniques 119 5.1 Brief Overview of Clustering 119 5.2 Partitioning 120 5.3 Hierarchies 125 5.4 Illustration 131 5.5 Other Issues 146 6 Partitioning Techniques 149 6.1 Basic Partitioning Concepts 150 6.2 Multi-valued List Observations 153 6.3 Interval-valued Data 159 6.4 Histogram Observations 169 6.5 Mixed-valued Observations 177 6.6 Mixture Distribution Methods 179 6.7 Cluster Representation 186 6.8 Other Issues 189 Exercises 191 Appendix 193 7 Divisive Hierarchical Clustering 197 7.1 Some Basics 197 7.1.1 Partitioning Criteria 197 7.1.2 Association Measures 200 7.2 Monothetic Methods 203 7.2.1 Modal Multi-valued Observations 205 7.2.2 Non-modal Multi-valued Observations 214 7.2.3 Interval-valued Observations 216 7.2.4 Histogram-valued Observations 225 7.3 Polythethic Methods 236 7.4 Stopping Rule R 250 7.5 Other Issues 257 Exercises 258 8 Agglomerative Hierarchical Clustering 261 8.1 Agglomerative Hierarchical Clustering 261 8.1.1 Some Basic Definitions 261 8.1.2 Multi-valued List Observations 266 8.1.3 Interval-valued Observations 269 8.1.4 Histogram-valued Observations 278 8.1.5 Mixed-valued Observations 281 8.1.6 Interval Observations with Rules 282 8.2 Pyramidal Clustering 289 8.2.1 Generality Degree 289 8.2.2 Pyramid Construction Based on Generality Degree 297 8.2.3 Pyramids from Dissimilarity Matrix 309 8.2.4 Other Issues 312 Exercises 313 Appendix 315 References 317 Index 331

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Book SynopsisGraphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.Trade Review“The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.” (Zentralblatt Math, 1 August 2013) "All of the necessary background is provided, with material on modeling under uncertainty and imprecision modeling, decomposition of distributions, graphical representation of distributions, applications relating to graphical models, and problems for further research." (Book News, December 2009)Table of ContentsPreface. 1 Introduction. 1.1 Data and Knowledge. 1.2 Knowledge Discovery and Data Mining. 1.3 Graphical Models. 1.4 Outline of this Book. 2 Imprecision and Uncertainty. 2.1 Modeling Inferences. 2.2 Imprecision and Relational Algebra. 2.3 Uncertainty and Probability Theory. 2.4 Possibility Theory and the Context Model. 3 Decomposition. 3.1 Decomposition and Reasoning. 3.2 Relational Decomposition. 3.3 Probabilistic Decomposition. 3.4 Possibilistic Decomposition. 3.5 Possibility versus Probability. 4 Graphical Representation. 4.1 Conditional Independence Graphs. 4.2 Evidence Propagation in Graphs. 5 Computing Projections. 5.1 Databases of Sample Cases. 5.2 Relational and Sum Projections. 5.3 Expectation Maximization. 5.4 Maximum Projections. 6 Naive Classifiers. 6.1 Naive Bayes Classifiers. 6.2 A Naive Possibilistic Classifier. 6.3 Classifier Simplification. 6.4 Experimental Evaluation. 7 Learning Global Structure. 7.1 Principles of Learning Global Structure. 7.2 Evaluation Measures. 7.3 Search Methods. 7.4 Experimental Evaluation. 8 Learning Local Structure. 8.1 Local Network Structure. 8.2 Learning Local Structure. 8.3 Experimental Evaluation. 9 Inductive Causation. 9.1 Correlation and Causation. 9.2 Causal and Probabilistic Structure. 9.3 Faithfulness and Latent Variables. 9.4 The Inductive Causation Algorithm. 9.5 Critique of the Underlying Assumptions. 9.6 Evaluation. 10 Visualization. 10.1 Potentials. 10.2 Association Rules. 11 Applications. 11.1 Diagnosis of Electrical Circuits. 11.2 Application in Telecommunications. 11.3 Application at Volkswagen. 11.4 Application at DaimlerChrysler. A Proofs of Theorems. A.1 Proof of Theorem 4.1.2. A.2 Proof of Theorem 4.1.18. A.3 Proof of Theorem 4.1.20. A.4 Proof of Theorem 4.1.26. A.5 Proof of Theorem 4.1.28. A.6 Proof of Theorem 4.1.30. A.7 Proof of Theorem 4.1.31. A.8 Proof of Theorem 5.4.8. A.9 Proof of Lemma .2.2. A.10 Proof of Lemma .2.4. A.11 Proof of Lemma .2.6. A.12 Proof of Theorem 7.3.1. A.13 Proof of Theorem 7.3.2. A.14 Proof of Theorem 7.3.3. A.15 Proof of Theorem 7.3.5. A.16 Proof of Theorem 7.3.7. B Software Tools. Bibliography. Index.

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Book SynopsisCovers all the theory and practical advice that actuaries need in order to determine the claims reserves for non-life insurance. Describes all the necessary mathematical methods used to estimate loss reserves and shares the authors' practical experience, which is essential in showing which of the methods should be applied in any given situation.Table of ContentsPreface xi Acknowledgement xiii 1 Introduction and Notation 1 1.1 Claims process 1 1.1.1 Accounting principles and accident years 2 1.1.2 Inflation 3 1.2 Structural framework to the claims-reserving problem 5 1.2.1 Fundamental properties of the claims reserving process 7 1.2.2 Known and unknown claims 9 1.3 Outstanding loss liabilities, classical notation 10 1.4 General remarks 12 2 Basic Methods 15 2.1 Chain-ladder method (distribution-free) 15 2.2 Bornhuetter–Ferguson method 21 2.3 Number of IBNyR claims, Poisson model 25 2.4 Poisson derivation of the CL algorithm 27 3 Chain-Ladder Models 33 3.1 Mean square error of prediction 33 3.2 Chain-ladder method 36 3.2.1 Mack model (distribution-free CL model) 37 3.2.2 Conditional process variance 41 3.2.3 Estimation error for single accident years 44 3.2.4 Conditional MSEP, aggregated accident years 55 3.3 Bounds in the unconditional approach 58 3.3.1 Results and interpretation 58 3.3.2 Aggregation of accident years 63 3.3.3 Proof of Theorems 3.17, 3.18 and 3.20 64 3.4 Analysis of error terms in the CL method 70 3.4.1 Classical CL model 70 3.4.2 Enhanced CL model 71 3.4.3 Interpretation 72 3.4.4 CL estimator in the enhanced model 73 3.4.5 Conditional process and parameter prediction errors 74 3.4.6 CL factors and parameter estimation error 75 3.4.7 Parameter estimation 81 4 Bayesian Models 91 4.1 Benktander–Hovinen method and Cape–Cod model 91 4.1.1 Benktander–Hovinen method 92 4.1.2 Cape–Cod model 95 4.2 Credible claims reserving methods 98 4.2.1 Minimizing quadratic loss functions 98 4.2.2 Distributional examples to credible claims reserving 101 4.2.3 Log-normal/Log-normal model 105 4.3 Exact Bayesian models 113 4.3.1 Overdispersed Poisson model with gamma prior distribution 114 4.3.2 Exponential dispersion family with its associated conjugates 122 4.4 Markov chain Monte Carlo methods 131 4.5 Bühlmann–Straub credibility model 145 4.6 Multidimensional credibility models 154 4.6.1 Hachemeister regression model 155 4.6.2 Other credibility models 159 4.7 Kalman filter 160 5 Distributional Models 167 5.1 Log-normal model for cumulative claims 167 5.1.1 Known variances σj 2 170 5.1.2 Unknown variances 177 5.2 Incremental claims 182 5.2.1 (Overdispersed) Poisson model 182 5.2.2 Negative-Binomial model 183 5.2.3 Log-normal model for incremental claims 185 5.2.4 Gamma model 186 5.2.5 Tweedie’s compound Poisson model 188 5.2.6 Wright’s model 199 6 Generalized Linear Models 201 6.1 Maximum likelihood estimators 201 6.2 Generalized linear models framework 203 6.3 Exponential dispersion family 205 6.4 Parameter estimation in the EDF 208 6.4.1 MLE for the EDF 208 6.4.2 Fisher’s scoring method 210 6.4.3 Mean square error of prediction 214 6.5 Other GLM models 223 6.6 Bornhuetter–Ferguson method, revisited 223 6.6.1 MSEP in the BF method, single accident year 226 6.6.2 MSEP in the BF method, aggregated accident years 230 7 Bootstrap Methods 233 7.1 Introduction 233 7.1.1 Efron’s non-parametric bootstrap 234 7.1.2 Parametric bootstrap 236 7.2 Log-normal model for cumulative sizes 237 7.3 Generalized linear models 242 7.4 Chain-ladder method 244 7.4.1 Approach 1: Unconditional estimation error 246 7.4.2 Approach 3: Conditional estimation error 247 7.5 Mathematical thoughts about bootstrapping methods 248 7.6 Synchronous bootstrapping of seemingly unrelated regressions 253 8 Multivariate Reserving Methods 257 8.1 General multivariate framework 257 8.2 Multivariate chain-ladder method 259 8.2.1 Multivariate CL model 259 8.2.2 Conditional process variance 264 8.2.3 Conditional estimation error for single accident years 265 8.2.4 Conditional MSEP, aggregated accident years 272 8.2.5 Parameter estimation 274 8.3 Multivariate additive loss reserving method 288 8.3.1 Multivariate additive loss reserving model 288 8.3.2 Conditional process variance 295 8.3.3 Conditional estimation error for single accident years 295 8.3.4 Conditional MSEP, aggregated accident years 297 8.3.5 Parameter estimation 299 8.4 Combined Multivariate CL and ALR method 308 8.4.1 Combined CL and ALR method: the model 308 8.4.2 Conditional cross process variance 313 8.4.3 Conditional cross estimation error for single accident years 315 8.4.4 Conditional MSEP, aggregated accident years 319 8.4.5 Parameter estimation 321 9 Selected Topics I: Chain-Ladder Methods 331 9.1 Munich chain-ladder 331 9.1.1 The Munich chain-ladder model 333 9.1.2 Credibility approach to the MCL method 335 9.1.3 MCL Parameter estimation 340 9.2 CL Reserving: A Bayesian inference model 346 9.2.1 Prediction of the ultimate claim 351 9.2.2 Likelihood function and posterior distribution 351 9.2.3 Mean square error of prediction 354 9.2.4 Credibility chain-ladder 359 9.2.5 Examples 361 9.2.6 Markov chain Monte Carlo methods 364 10 Selected Topics II: Individual Claims Development Processes 369 10.1 Modelling claims development processes for individual claims 369 10.1.1 Modelling framework 370 10.1.2 Claims reserving categories 376 10.2 Separating IBNeR and IBNyR claims 379 11 Statistical Diagnostics 391 11.1 Testing age-to-age factors 391 11.1.1 Model choice 394 11.1.2 Age-to-age factors 396 11.1.3 Homogeneity in time and distributional assumptions 398 11.1.4 Correlations 399 11.1.5 Diagonal effects 401 11.2 Non-parametric smoothing 401 Appendix A: Distributions 405 A.1 Discrete distributions 405 A.1.1 Binomial distribution 405 A.1.2 Poisson distribution 405 A.1.3 Negative-Binomial distribution 405 A.2 Continuous distributions 406 A.2.1 Uniform distribution 406 A.2.2 Normal distribution 406 A.2.3 Log-normal distribution 407 A.2.4 Gamma distribution 407 A.2.5 Beta distribution 408 Bibliography 409 Index 417

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Book SynopsisWritten by authorities in the field, Lower Previsions illustrates how the theory of Lower Previsions can be extended to cover a larger set of random quantities. The text highlights a crucial problem in the theory of imprecise probability and provides a detailed theory on how to resolve it.Table of ContentsPreface xv Acknowledgements xvii 1 Preliminary notions and definitions 1 1.1 Sets of numbers 1 1.2 Gambles 2 1.3 Subsets and their indicators 5 1.4 Collections of events 5 1.5 Directed sets and Moore–Smith limits 7 1.6 Uniform convergence of bounded gambles 9 1.7 Set functions, charges and measures 10 1.8 Measurability and simple gambles 12 1.9 Real functionals 17 1.10 A useful lemma 19 PART I LOWER PREVISIONS ON BOUNDED GAMBLES 21 2 Introduction 23 3 Sets of acceptable bounded gambles 25 3.1 Random variables 26 3.2 Belief and behaviour 27 3.3 Bounded gambles 28 3.4 Sets of acceptable bounded gambles 29 3.4.1 Rationality criteria 29 3.4.2 Inference 32 4 Lower previsions 37 4.1 Lower and upper previsions 38 4.1.1 From sets of acceptable bounded gambles to lower previsions 38 4.1.2 Lower and upper previsions directly 40 4.2 Consistency for lower previsions 41 4.2.1 Definition and justification 41 4.2.2 A more direct justification for the avoiding sure loss condition 44 4.2.3 Avoiding sure loss and avoiding partial loss 45 4.2.4 Illustrating the avoiding sure loss condition 45 4.2.5 Consequences of avoiding sure loss 46 4.3 Coherence for lower previsions 46 4.3.1 Definition and justification 46 4.3.2 A more direct justification for the coherence condition 50 4.3.3 Illustrating the coherence condition 51 4.3.4 Linear previsions 51 4.4 Properties of coherent lower previsions 53 4.4.1 Interesting consequences of coherence 53 4.4.2 Coherence and conjugacy 56 4.4.3 Easier ways to prove coherence 56 4.4.4 Coherence and monotone convergence 63 4.4.5 Coherence and a seminorm 64 4.5 The natural extension of a lower prevision 65 4.5.1 Natural extension as least-committal extension 65 4.5.2 Natural extension and equivalence 66 4.5.3 Natural extension to a specific domain 66 4.5.4 Transitivity of natural extension 67 4.5.5 Natural extension and avoiding sure loss 67 4.5.6 Simpler ways of calculating the natural extension 69 4.6 Alternative characterisations for avoiding sure loss, coherence, and natural extension 70 4.7 Topological considerations 74 5 Special coherent lower previsions 76 5.1 Linear previsions on finite spaces 77 5.2 Coherent lower previsions on finite spaces 78 5.3 Limits as linear previsions 80 5.4 Vacuous lower previsions 81 5.5 {0, 1}-valued lower probabilities 82 5.5.1 Coherence and natural extension 82 5.5.2 The link with classical propositional logic 88 5.5.3 The link with limits inferior 90 5.5.4 Monotone convergence 91 5.5.5 Lower oscillations and neighbourhood filters 93 5.5.6 Extending a lower prevision defined on all continuous bounded gambles 98 6 n-Monotone lower previsions 101 6.1 n-Monotonicity 102 6.2 n-Monotonicity and coherence 107 6.2.1 A few observations 107 6.2.2 Results for lower probabilities 109 6.3 Representation results 113 7 Special n-monotone coherent lower previsions 122 7.1 Lower and upper mass functions 123 7.2 Minimum preserving lower previsions 127 7.2.1 Definition and properties 127 7.2.2 Vacuous lower previsions 128 7.3 Belief functions 128 7.4 Lower previsions associated with proper filters 129 7.5 Induced lower previsions 131 7.5.1 Motivation 131 7.5.2 Induced lower previsions 133 7.5.3 Properties of induced lower previsions 134 7.6 Special cases of induced lower previsions 138 7.6.1 Belief functions 139 7.6.2 Refining the set of possible values for a random variable 139 7.7 Assessments on chains of sets 142 7.8 Possibility and necessity measures 143 7.9 Distribution functions and probability boxes 147 7.9.1 Distribution functions 147 7.9.2 Probability boxes 149 8 Linear previsions, integration and duality 151 8.1 Linear extension and integration 153 8.2 Integration of probability charges 159 8.3 Inner and outer set function, completion and other extensions 163 8.4 Linear previsions and probability charges 166 8.5 The S-integral 168 8.6 The Lebesgue integral 171 8.7 The Dunford integral 172 8.8 Consequences of duality 177 9 Examples of linear extension 181 9.1 Distribution functions 181 9.2 Limits inferior 182 9.3 Lower and upper oscillations 183 9.4 Linear extension of a probability measure 183 9.5 Extending a linear prevision from continuous bounded gambles 187 9.6 Induced lower previsions and random sets 188 10 Lower previsions and symmetry 191 10.1 Invariance for lower previsions 192 10.1.1 Definition 192 10.1.2 Existence of invariant lower previsions 194 10.1.3 Existence of strongly invariant lower previsions 195 10.2 An important special case 200 10.3 Interesting examples 205 10.3.1 Permutation invariance on finite spaces 205 10.3.2 Shift invariance and Banach limits 208 10.3.3 Stationary random processes 210 11 Extreme lower previsions 214 11.1 Preliminary results concerning real functionals 215 11.2 Inequality preserving functionals 217 11.2.1 Definition 217 11.2.2 Linear functionals 217 11.2.3 Monotone functionals 218 11.2.4 n-Monotone functionals 218 11.2.5 Coherent lower previsions 219 11.2.6 Combinations 220 11.3 Properties of inequality preserving functionals 220 11.4 Infinite non-negative linear combinations of inequality preserving functionals 221 11.4.1 Definition 221 11.4.2 Examples 222 11.4.3 Main result 223 11.5 Representation results 224 11.6 Lower previsions associated with proper filters 225 11.6.1 Belief functions 225 11.6.2 Possibility measures 226 11.6.3 Extending a linear prevision defined on all continuous bounded gambles 226 11.6.4 The connection with induced lower previsions 227 11.7 Strongly invariant coherent lower previsions 228 PART II EXTENDING THE THEORY TO UNBOUNDED GAMBLES 231 12 Introduction 233 13 Conditional lower previsions 235 13.1 Gambles 236 13.2 Sets of acceptable gambles 236 13.2.1 Rationality criteria 236 13.2.2 Inference 238 13.3 Conditional lower previsions 240 13.3.1 Going from sets of acceptable gambles to conditional lower previsions 240 13.3.2 Conditional lower previsions directly 252 13.4 Consistency for conditional lower previsions 254 13.4.1 Definition and justification 254 13.4.2 Avoiding sure loss and avoiding partial loss 257 13.4.3 Compatibility with the definition for lower previsions on bounded gambles 258 13.4.4 Comparison with avoiding sure loss for lower previsions on bounded gambles 258 13.5 Coherence for conditional lower previsions 259 13.5.1 Definition and justification 259 13.5.2 Compatibility with the definition for lower previsions on bounded gambles 264 13.5.3 Comparison with coherence for lower previsions on bounded gambles 264 13.5.4 Linear previsions 264 13.6 Properties of coherent conditional lower previsions 266 13.6.1 Interesting consequences of coherence 266 13.6.2 Trivial extension 269 13.6.3 Easier ways to prove coherence 270 13.6.4 Separate coherence 278 13.7 The natural extension of a conditional lower prevision 279 13.7.1 Natural extension as least-committal extension 280 13.7.2 Natural extension and equivalence 281 13.7.3 Natural extension to a specific domain and the transitivity of natural extension 282 13.7.4 Natural extension and avoiding sure loss 283 13.7.5 Simpler ways of calculating the natural extension 285 13.7.6 Compatibility with the definition for lower previsions on bounded gambles 286 13.8 Alternative characterisations for avoiding sure loss, coherence and natural extension 287 13.9 Marginal extension 288 13.10 Extending a lower prevision from bounded gambles to conditional gambles 295 13.10.1 General case 295 13.10.2 Linear previsions and probability charges 297 13.10.3 Vacuous lower previsions 298 13.10.4 Lower previsions associated with proper filters 300 13.10.5 Limits inferior 300 13.11 The need for infinity? 301 14 Lower previsions for essentially bounded gambles 304 14.1 Null sets and null gambles 305 14.2 Null bounded gambles 310 14.3 Essentially bounded gambles 311 14.4 Extension of lower and upper previsions to essentially bounded gambles 316 14.5 Examples 322 14.5.1 Linear previsions and probability charges 322 14.5.2 Vacuous lower previsions 323 14.5.3 Lower previsions associated with proper filters 323 14.5.4 Limits inferior 324 14.5.5 Belief functions 325 14.5.6 Possibility measures 325 15 Lower previsions for previsible gambles 327 15.1 Convergence in probability 328 15.2 Previsibility 331 15.3 Measurability 340 15.4 Lebesgue’s dominated convergence theorem 343 15.5 Previsibility by cuts 348 15.6 A sufficient condition for previsibility 350 15.7 Previsibility for 2-monotone lower previsions 352 15.8 Convex combinations 355 15.9 Lower envelope theorem 355 15.10 Examples 358 15.10.1 Linear previsions and probability charges 358 15.10.2 Probability density functions: The normal density 359 15.10.3 Vacuous lower previsions 360 15.10.4 Lower previsions associated with proper filters 361 15.10.5 Limits inferior 361 15.10.6 Belief functions 362 15.10.7 Possibility measures 362 15.10.8 Estimation 365 Appendix A Linear spaces, linear lattices and convexity 368 Appendix B Notions and results from topology 371 B.1 Basic definitions 371 B.2 Metric spaces 372 B.3 Continuity 373 B.4 Topological linear spaces 374 B.5 Extreme points 374 Appendix C The Choquet integral 376 C.1 Preliminaries 376 C.1.1 The improper Riemann integral of a non-increasing function 376 C.1.2 Comonotonicity 378 C.2 Definition of the Choquet integral 378 C.3 Basic properties of the Choquet integral 379 C.4 A simple but useful equality 387 C.5 A simplified version of Greco’s representation theorem 389 Appendix D The extended real calculus 391 D.1 Definitions 391 D.2 Properties 392 Appendix E Symbols and notation 396 References 398 Index 407

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Book SynopsisBatch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized. Key Features: A thorough introduction to Batch Effects and Noise in Microrarray Experiments. A unique compilation of review and research articles on handling of batch effects and technical and biological noise in microarray data. An extensive overview of current standardization initiatives. All datasets aTable of ContentsList of Contributors xiii Foreword xvii Preface xix 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction 1Andreas Scherer 2 Microarray Platforms and Aspects of Experimental Variation 5John A Coller Jr 2.1 Introduction 5 2.2 Microarray Platforms 6 2.2.1 Affymetrix 6 2.2.2 Agilent 7 2.2.3 Illumina 7 2.2.4 Nimblegen 8 2.2.5 Spotted Microarrays 8 2.3 Experimental Considerations 9 2.3.1 Experimental Design 9 2.3.2 Sample and RNA Extraction 9 2.3.3 Amplification 12 2.3.4 Labeling 13 2.3.5 Hybridization 13 2.3.6 Washing 14 2.3.7 Scanning 15 2.3.8 Image Analysis and Data Extraction 16 2.3.9 Clinical Diagnosis 17 2.3.10 Interpretation of the Data 17 2.4 Conclusions 17 3 Experimental Design 19Peter Grass 3.1 Introduction 19 3.2 Principles of Experimental Design 20 3.2.1 Definitions 20 3.2.2 Technical Variation 21 3.2.3 Biological Variation 21 3.2.4 Systematic Variation 22 3.2.5 Population, Random Sample, Experimental and Observational Units 22 3.2.6 Experimental Factors 22 3.2.7 Statistical Errors 23 3.3 Measures to Increase Precision and Accuracy 24 3.3.1 Randomization 25 3.3.2 Blocking 25 3.3.3 Replication 25 3.3.4 Further Measures to Optimize Study Design 26 3.4 Systematic Errors in Microarray Studies 28 3.4.1 Selection Bias 28 3.4.2 Observational Bias 28 3.4.3 Bias at Specimen/Tissue Collection 29 3.4.4 Bias at mRNA Extraction and Hybridization 30 3.5 Conclusion 30 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies 33Naomi Altman 4.1 Introduction 33 4.1.1 Batch Effects 35 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments 35 4.2.1 Using the Linear Model for Design 37 4.2.2 Examples of Design Guided by the Linear Model 37 4.3 Blocks and Batches 39 4.3.1 Complete Block Designs 39 4.3.2 Incomplete Block Designs 39 4.3.3 Multiple Batch Effects 40 4.4 Reducing Batch Effects by Normalization and Statistical Adjustment 41 4.4.1 Between and Within Batch Normalization with Multi-array Methods 43 4.4.2 Statistical Adjustment 46 4.5 Sample Pooling and Sample Splitting 47 4.5.1 Sample Pooling 47 4.5.2 Sample Splitting: Technical Replicates 48 4.6 Pilot Experiments 49 4.7 Conclusions 49 Acknowledgements 50 5 Aspects of Technical Bias 51Martin Schumacher, Frank Staedtler, Wendell D Jones, and Andreas Scherer 5.1 Introduction 51 5.2 Observational Studies 52 5.2.1 Same Protocol, Different Times of Processing 52 5.2.2 Same Protocol, Different Sites (Study 1) 53 5.2.3 Same Protocol, Different Sites (Study 2) 55 5.2.4 Batch Effect Characteristics at the Probe Level 57 5.3 Conclusion 60 6 Bioinformatic Strategies for cDNA-Microarray Data Processing 61Jessica Fahlén, Mattias Landfors, Eva Freyhult, Max Bylesjö, Johan Trygg, Torgeir R Hvidsten, and Patrik Rydén 6.1 Introduction 61 6.1.1 Spike-in Experiments 62 6.1.2 Key Measures – Sensitivity and Bias 63 6.1.3 The IC Curve and MA Plot 63 6.2 Pre-processing 64 6.2.1 Scanning Procedures 65 6.2.2 Background Correction 65 6.2.3 Saturation 67 6.2.4 Normalization 68 6.2.5 Filtering 70 6.3 Downstream Analysis 71 6.3.1 Gene Selection 71 6.3.2 Cluster Analysis 71 6.4 Conclusion 73 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance 75Nysia I George and James J Chen 7.1 Introduction 75 7.1.1 Microarray Gene Expression Data 76 7.1.2 Analysis of Variance in Gene Expression Data 77 7.2 Variance Component Analysis across Microarray Platforms 78 7.3 Methodology 78 7.3.1 Data Description 78 7.3.2 Normalization 79 7.3.3 Gene-Specific ANOVA Model 81 7.4 Application: The MAQC Project 81 7.5 Discussion and Conclusion 85 Acknowledgements 85 8 Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set 87Michael J Boedigheimer, Jeff W Chou, J Christopher Corton, Jennifer Fostel, Raegan O’Lone, P Scott Pine, John Quackenbush, Karol L Thompson, and Russell D Wolfinger 8.1 Introduction 87 8.2 Methodology 89 8.3 Results 89 8.3.1 Assessment of Smooth Bias in Baseline Expression Data Sets 89 8.3.2 Relationship between Smooth Bias and Signal Detection 91 8.3.3 Effect of Smooth Bias Correction on Principal Components Analysis 92 8.3.4 Effect of Smooth Bias Correction on Estimates of Attributable Variability 94 8.3.5 Effect of Smooth Bias Correction on Detection of Genes Differentially Expressed by Fasting 95 8.3.6 Effect of Smooth Bias Correction on the Detection of Strain-Selective Gene Expression 96 8.4 Discussion 97 Acknowledgements 99 9 Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions 101Walter Liggett, Jean Lozach, Anne Bergstrom Lucas, Ron L Peterson, Marc L Salit, Danielle Thierry-Mieg, Jean Thierry-Mieg, and Russell D Wolfinger 9.1 Introduction 101 9.2 Input Mass Effect on the Amount of Normalization Applied 103 9.3 Probe-by-Probe Modeling of the Input Mass Effect 103 9.4 Further Evidence of Batch Effects 108 9.5 Conclusions 110 10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods 113W Evan Johnson and Cheng li 10.1 Introduction 113 10.1.1 Bayesian and Empirical Bayes Applications in Microarrays 114 10.2 Existing Methods for Adjusting Batch Effect 115 10.2.1 Microarray Data Normalization 115 10.2.2 Batch Effect Adjustment Methods for Large Sample Size 115 10.2.3 Model-Based Location and Scale Adjustments 116 10.3 Empirical Bayes Method for Adjusting Batch Effect 117 10.3.1 Parametric Shrinkage Adjustment 117 10.3.2 Empirical Bayes Batch Effect Parameter Estimates using Nonparametric Empirical Priors 120 10.4 Data Examples, Results and Robustness of the Empirical Bayes Method 121 10.4.1 Microarray Data with Batch Effects 121 10.4.2 Results for Data Set 1 124 10.4.3 Results for Data Set 2 124 10.4.4 Robustness of the Empirical Bayes Method 126 10.4.5 Software Implementation 127 10.5 Discussion 128 11 Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis 131Wynn L Walker and Frank R Sharp 11.1 Introduction 131 11.2 Methodology 133 11.2.1 Data Description 133 11.2.2 Empirical Bayes Method for Batch Adjustment 134 11.2.3 Naïve t-test Batch Adjustment 135 11.3 Application: Expression Profiling of Blood from Muscular Dystrophy Patients 135 11.3.1 Removal of Cross-Experimental Batch Effects 135 11.3.2 Removal of Within-Experimental Batch Effects 136 11.3.3 Removal of Batch Effects: Empirical Bayes Method versus t-Test Filter 137 11.4 Discussion and Conclusion 138 11.4.1 Methods for Batch Adjustment Within and Across Experiments 138 11.4.2 Bayesian Approach is Well Suited for Modeling Cross-Experimental Batch Effects 139 11.4.3 Implications of Cross-Experimental Batch Corrections for Clinical Studies 139 12 Principal Variance Components Analysis: Estimating Batch Effects in Microarray Gene Expression Data 141Jianying Li, Pierre R Bushel, Tzu-Ming Chu, and Russell D Wolfinger 12.1 Introduction 141 12.2 Methods 143 12.2.1 Principal Components Analysis 143 12.2.2 Variance Components Analysis and Mixed Models 145 12.2.3 Principal Variance Components Analysis 145 12.3 Experimental Data 146 12.3.1 A Transcription Inhibition Study 146 12.3.2 A Lung Cancer Toxicity Study 147 12.3.3 A Hepato-toxicant Toxicity Study 147 12.4 Application of the PVCA Procedure to the Three Example Data Sets 148 12.4.1 PVCA Provides Detailed Estimates of Batch Effects 148 12.4.2 Visualizing the Sources of Batch Effects 149 12.4.3 Selecting the Principal Components in the Modeling 150 12.5 Discussion 153 13 Batch Profile Estimation, Correction, and Scoring 155Tzu-Ming Chu, Wenjun Bao, Russell S Thomas, and Russell D Wolfinger 13.1 Introduction 155 13.2 Mouse Lung Tumorigenicity Data Set with Batch Effects 157 13.2.1 Batch Profile Estimation 159 13.2.2 Batch Profile Correction 160 13.2.3 Batch Profile Scoring 161 13.2.4 Cross-Validation Results 162 13.3 Discussion 164 Acknowledgements 165 14 Visualization of Cross-Platform Microarray Normalization 167Xuxin Liu, Joel Parker, Cheng Fan, Charles M Perou, and J S Marron 14.1 Introduction 167 14.2 Analysis of the NCI 60 Data 169 14.3 Improved Statistical Power 174 14.4 Gene-by-Gene versus Multivariate Views 178 14.5 Conclusion 181 15 Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis 183Lev Klebanov and Andreas Scherer 15.1 Introduction 183 15.2 Aggregated Expression Intensities 185 15.3 Covariance between Log-Expressions 186 15.4 Conclusion 189 Acknowledgements 190 16 Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies 191Huixiao Hong, Leming Shi, James C Fuscoe, Federico Goodsaid, Donna Mendrick, and Weida Tong 16.1 Introduction 191 16.2 Potential Sources of Spurious Associations 192 16.2.1 Spurious Associations Related to Study Design 194 16.2.2 Spurious Associations Caused in Genotyping Experiments 195 16.2.3 Spurious Associations Caused by Genotype Calling Errors 195 16.3 Batch Effects 196 16.3.1 Batch Effect in Genotyping Experiment 196 16.3.2 Batch Effect in Genotype Calling 197 16.4 Conclusion 201 Disclaimer 201 17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development 203Khurram Shahzad, Anshu Sinha, Farhana Latif, and Mario C Deng 17.1 Introduction 203 17.2 Theoretical Framework 204 17.3 Systems-Biological Concepts in Medicine 204 17.4 General Conceptual Challenges 205 17.5 Strategies for Gene Expression Biomarker Development 205 17.5.1 Phase 1: Clinical Phenotype Consensus Definition 206 17.5.2 Phase 2: Gene Discovery 207 17.5.3 Phase 3: Internal Differential Gene List Confirmation 209 17.5.4 Phase 4: Diagnostic Classifier Development 209 17.5.5 Phase 5: External Clinical Validation 210 17.5.6 Phase 6: Clinical Implementation 211 17.5.7 Phase 7: Post-Clinical Implementation Studies 212 17.6 Conclusions 213 18 Data, Analysis, and Standardization 215Gabriella Rustici, Andreas Scherer, and John Quackenbush 18.1 Introduction 215 18.2 Reporting Standards 216 18.3 Computational Standards: From Microarray to Omic Sciences 219 18.3.1 The Microarray Gene Expression Data Society 219 18.3.2 The Proteomics Standards Initiative 220 18.3.3 The Metabolomics Standards Initiative 220 18.3.4 The Genomic Standards Consortium 220 18.3.5 Systems Biology Initiatives 221 18.3.6 Data Standards in Biopharmaceutical and Clinical Research 221 18.3.7 Standards Integration Initiatives 222 18.3.8 The MIBBI project 223 18.3.9 OBO Foundry 223 18.3.10 FuGE and ISA-TAB 223 18.4 Experimental Standards: Developing Quality Metrics and a Consensus on Data Analysis Methods 226 18.5 Conclusions and Future Perspective 228 References 231 Index 245

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Book SynopsisThis book aims to enable readers to understand and implement, via the widely used statistical software package Minitab (Release 16), statistical methods fundamental to the Six Sigma approach to the continuous improvement of products, processes and services.Table of ContentsForeword. Preface. Acknowledgements. About the Author. 1 Introduction. 1.1 Quality and Quality Improvement. 1.2 Six Sigma Quality Improvement. 1.3 The Six Sigma Roadmap and DMAIC. 1.4 The Role of Statistical Methods in Six Sigma. 1.5 Minitab and its Role in the Implementation of Statistical Methods. 1.6 Exercises and Follow-Up Activities. 2 Data Display, Summary and Manipulation. 2.1 The Run Chart – a First Minitab Session. 2.1.1 Input of Data Via Keyboard and Creation of a Run Chart in Minitab. 2.1.2 Minitab Projects and Their Components. 2.2 Display and Summary of Univariate Data. 2.2.1 Histogram and Distribution. 2.2.2 Shape of a Distribution. 2.2.3 Location. 2.2.4 Variability. 2.3 Data Input, Output, Manipulation and Management. 2.3.1 Data Input and Output. 2.3.2 Stacking and Unstacking of Data; Changing Data Type and Coding. 2.3.3 Case Study Demonstrating Ranking, Sorting and Extraction of Information from Date/Time Data. 2.4 Exercises and Follow-Up Activities. 3 Exploratory Data Analysis, Display and Summary of Multivariate Data. 3.1 Exploratory Data Analysis. 3.1.1 Stem-and-Leaf Displays. 3.1.2 Outliers and Outlier Detection. 3.1.3 Boxplots. 3.1.4 Brushing. 3.2 Display and Summary of Bivariate and Multivariate Data. 3.2.1 Bivariate Data – Scatterplots and Marginal Plots. 3.2.2 Covariance and Correlation. 3.2.3 Multivariate Data – Matrix Plots. 3.2.4 Multi-Vari Charts. 3.3 Other Displays. 3.3.1 Pareto Charts. 3.3.2 Cause-and-Effect Diagrams. 3.4 Exercises and Follow-Up Activities. 4 Statistical Models. 4.1 Fundamentals of Probability. 4.1.1 Concept and Notation. 4.1.2 Rules for Probabilities. 4.2 Probability Distributions for Counts and Measurements. 4.2.1 Binomial Distribution. 4.2.2 Poisson Distribution. 4.2.3 Normal (Gaussian) Distribution. 4.3 Distribution of Means and Proportions. 4.3.1 Two Preliminary Results. 4.3.2 Distribution of the Sample Mean. 4.3.3 Distribution of the Sample Proportion. 4.4 Multivariate Normal Distribution. 4.5 Statistical Models Applied to Acceptance Sampling. 4.5.1 Acceptance Sampling by Attributes. 4.5.2 Acceptance Sampling by Variables. 4.6 Exercises and Follow-Up Activities. 5 Control Charts. 5.1 Shewhart Charts for Measurement Data. 5.1.1 I and MR Charts for Individual Measurements. 5.1.2 Tests for Evidence of Special Cause Variation on Shewhart Charts. 5.1.3 Xbar and R Charts for Samples (Subgroups) of Measurements. 5.2 Shewhart Charts for Attribute Data. 5.2.1 P Chart for Proportion Nonconforming. 5.2.2 NP Chart for Number Nonconforming. 5.2.3 C Chart for Count of Nonconformities. 5.2.4 U Chart for Nonconformities Per Unit. 5.2.5 Funnel Plots. 5.3 Time-Weighted Control Charts. 5.3.1 Moving Averages and their Applications. 5.3.2 Exponentially Weighted Moving Average Control Charts. 5.3.3 Cumulative Sum Control Charts. 5.4 Process Adjustment. 5.4.1 Process Tampering. 5.4.2 Autocorrelated Data and Process Feedback Adjustment. 5.5 Multivariate Control Charts. 5.6 Exercises and Follow-Up Activities. 6 Process Capability Analysis. 6.1 Process Capability. 6.1.1 Process Capability Analysis with Measurement Data. 6.1.2 Process Capability Indices and Sigma Quality Levels. 6.1.3 Process Capability Analysis with Nonnormal Data. 6.1.4 Tolerance Intervals. 6.1.5 Process Capability Analysis with Attribute Data. 6.2 Exercises and Follow-Up Activities. 7 Process Experimentation with a Single Factor. 7.1 Fundamentals of Hypothesis Testing. 7.2 Tests and Confidence Intervals for the Comparison of Means and Proportions with a Standard. 7.2.1 Tests Based on the Standard Normal Distribution – z-Tests. 7.2.2 Tests Based on the Student t-Distribution – t-Tests. 7.2.3 Tests for Proportions. 7.2.4 Nonparametric Sign and Wilcoxon Tests. 7.3 Tests and Confidence Intervals for the Comparison of Two Means or Two Proportions. 7.3.1 Two-Sample t-Tests. 7.3.2 Tests for Two Proportions. 7.3.3 Nonparametric Mann–Whitney Test. 7.4 The Analysis of Paired Data – t-Tests and Sign Tests. 7.5 Experiments with a Single Factor Having More Than Two Levels. 7.5.1 Design and Analysis of a Single-Factor Experiment. 7.5.2 The Fixed Effects Model. 7.5.3 The Random Effects Model. 7.5.4 The Nonparametric Kruskal–Wallis Test. 7.6 Blocking in Single-Factor Experiments. 7.7 Experiments with a Single Factor, with More Than Two Levels, where the Response is a Proportion. 7.8 Tests for Equality of Variances. 7.9 Exercises and Follow-Up Activities. 8 Process Experimentation with Two or More Factors. 8.1 General Factorial Experiments. 8.1.1 Creation of a General Factorial Experimental Design. 8.1.2 Display and Analysis of Data from a General Factorial Experiment. 8.1.3 The Fixed Effects Model, Comparisons. 8.1.4 The Random Effects Model, Components of Variance. 8.2 Full Factorial Experiments in the 2k Series. 8.2.1 22 Factorial Experimental Designs, Display and Analysis of Data. 8.2.2 Models and Associated Displays. 8.2.3 Examples of 23 and 24 Experiments, the Use of Pareto and Normal Probability Plots of Effects. 8.3 Fractional Factorial Experiments in the 2k-p Series. 8.3.1 Introduction to Fractional Factorial Experiments, Confounding and Resolution. 8.3.2 Case Study Examples. 8.4 Taguchi Experimental Designs. 8.5 Exercises and Follow-Up Activities. 9 Evaluation of Measurement Processes. 9.1 Measurement Process Concepts. 9.1.1 Bias, Linearity, Repeatability and Reproducibility. 9.1.2 Inadequate Measurement Units. 9.2 Gauge Repeatability and Reproducibility Studies. 9.3 Comparison of Measurement Systems. 9.4 Attribute Scenarios. 9.5 Exercises and Follow-Up Activities. 10 Regression and Model Building. 10.1 Regression with a Single Predictor Variable. 10.2 Multiple Regression. 10.3 Response Surface Methods. 10.4 Categorical Data and Logistic Regression. 10.4.1 Tests of Association Using the Chi-Square Distribution. 10.4.2 Binary Logistic Regression. 10.5 Exercises and Follow-Up Activities. 11 Learning More and Further Minitab. 11.1 Learning More about Minitab and Obtaining Help. 11.1.1 Meet Minitab. 11.1.2 Help. 11.1.3 StatGuide. 11.1.4 Tutorials. 11.1.5 Assistant. 11.1.6 Glossary, Methods and Formulas. 11.1.7 Minitab on the Web and Knowledgebase/FAQ. 11.2 Macros. 11.2.1 Minitab Session Commands. 11.2.2 Global and Local Minitab Macros. 11.3 Further Features of Minitab. 11.4 Quality Companion. 11.5 Postscript. Appendix 1. Appendix 2. Appendix 3. Appendix 4. References. Index.

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Book SynopsisThis book aims to enable readers to understand and implement, via the widely used statistical software package Minitab (Release 16), statistical methods fundamental to the Six Sigma approach to the continuous improvement of products, processes and services.Table of ContentsForeword. Preface. Acknowledgements. About the Author. 1 Introduction. 1.1 Quality and Quality Improvement. 1.2 Six Sigma Quality Improvement. 1.3 The Six Sigma Roadmap and DMAIC. 1.4 The Role of Statistical Methods in Six Sigma. 1.5 Minitab and its Role in the Implementation of Statistical Methods. 1.6 Exercises and Follow-Up Activities. 2 Data Display, Summary and Manipulation. 2.1 The Run Chart – a First Minitab Session. 2.1.1 Input of Data Via Keyboard and Creation of a Run Chart in Minitab. 2.1.2 Minitab Projects and Their Components. 2.2 Display and Summary of Univariate Data. 2.2.1 Histogram and Distribution. 2.2.2 Shape of a Distribution. 2.2.3 Location. 2.2.4 Variability. 2.3 Data Input, Output, Manipulation and Management. 2.3.1 Data Input and Output. 2.3.2 Stacking and Unstacking of Data; Changing Data Type and Coding. 2.3.3 Case Study Demonstrating Ranking, Sorting and Extraction of Information from Date/Time Data. 2.4 Exercises and Follow-Up Activities. 3 Exploratory Data Analysis, Display and Summary of Multivariate Data. 3.1 Exploratory Data Analysis. 3.1.1 Stem-and-Leaf Displays. 3.1.2 Outliers and Outlier Detection. 3.1.3 Boxplots. 3.1.4 Brushing. 3.2 Display and Summary of Bivariate and Multivariate Data. 3.2.1 Bivariate Data – Scatterplots and Marginal Plots. 3.2.2 Covariance and Correlation. 3.2.3 Multivariate Data – Matrix Plots. 3.2.4 Multi-Vari Charts. 3.3 Other Displays. 3.3.1 Pareto Charts. 3.3.2 Cause-and-Effect Diagrams. 3.4 Exercises and Follow-Up Activities. 4 Statistical Models. 4.1 Fundamentals of Probability. 4.1.1 Concept and Notation. 4.1.2 Rules for Probabilities. 4.2 Probability Distributions for Counts and Measurements. 4.2.1 Binomial Distribution. 4.2.2 Poisson Distribution. 4.2.3 Normal (Gaussian) Distribution. 4.3 Distribution of Means and Proportions. 4.3.1 Two Preliminary Results. 4.3.2 Distribution of the Sample Mean. 4.3.3 Distribution of the Sample Proportion. 4.4 Multivariate Normal Distribution. 4.5 Statistical Models Applied to Acceptance Sampling. 4.5.1 Acceptance Sampling by Attributes. 4.5.2 Acceptance Sampling by Variables. 4.6 Exercises and Follow-Up Activities. 5 Control Charts. 5.1 Shewhart Charts for Measurement Data. 5.1.1 I and MR Charts for Individual Measurements. 5.1.2 Tests for Evidence of Special Cause Variation on Shewhart Charts. 5.1.3 Xbar and R Charts for Samples (Subgroups) of Measurements. 5.2 Shewhart Charts for Attribute Data. 5.2.1 P Chart for Proportion Nonconforming. 5.2.2 NP Chart for Number Nonconforming. 5.2.3 C Chart for Count of Nonconformities. 5.2.4 U Chart for Nonconformities Per Unit. 5.2.5 Funnel Plots. 5.3 Time-Weighted Control Charts. 5.3.1 Moving Averages and their Applications. 5.3.2 Exponentially Weighted Moving Average Control Charts. 5.3.3 Cumulative Sum Control Charts. 5.4 Process Adjustment. 5.4.1 Process Tampering. 5.4.2 Autocorrelated Data and Process Feedback Adjustment. 5.5 Multivariate Control Charts. 5.6 Exercises and Follow-Up Activities. 6 Process Capability Analysis. 6.1 Process Capability. 6.1.1 Process Capability Analysis with Measurement Data. 6.1.2 Process Capability Indices and Sigma Quality Levels. 6.1.3 Process Capability Analysis with Nonnormal Data. 6.1.4 Tolerance Intervals. 6.1.5 Process Capability Analysis with Attribute Data. 6.2 Exercises and Follow-Up Activities. 7 Process Experimentation with a Single Factor. 7.1 Fundamentals of Hypothesis Testing. 7.2 Tests and Confidence Intervals for the Comparison of Means and Proportions with a Standard. 7.2.1 Tests Based on the Standard Normal Distribution – z-Tests. 7.2.2 Tests Based on the Student t-Distribution – t-Tests. 7.2.3 Tests for Proportions. 7.2.4 Nonparametric Sign and Wilcoxon Tests. 7.3 Tests and Confidence Intervals for the Comparison of Two Means or Two Proportions. 7.3.1 Two-Sample t-Tests. 7.3.2 Tests for Two Proportions. 7.3.3 Nonparametric Mann–Whitney Test. 7.4 The Analysis of Paired Data – t-Tests and Sign Tests. 7.5 Experiments with a Single Factor Having More Than Two Levels. 7.5.1 Design and Analysis of a Single-Factor Experiment. 7.5.2 The Fixed Effects Model. 7.5.3 The Random Effects Model. 7.5.4 The Nonparametric Kruskal–Wallis Test. 7.6 Blocking in Single-Factor Experiments. 7.7 Experiments with a Single Factor, with More Than Two Levels, where the Response is a Proportion. 7.8 Tests for Equality of Variances. 7.9 Exercises and Follow-Up Activities. 8 Process Experimentation with Two or More Factors. 8.1 General Factorial Experiments. 8.1.1 Creation of a General Factorial Experimental Design. 8.1.2 Display and Analysis of Data from a General Factorial Experiment. 8.1.3 The Fixed Effects Model, Comparisons. 8.1.4 The Random Effects Model, Components of Variance. 8.2 Full Factorial Experiments in the 2k Series. 8.2.1 22 Factorial Experimental Designs, Display and Analysis of Data. 8.2.2 Models and Associated Displays. 8.2.3 Examples of 23 and 24 Experiments, the Use of Pareto and Normal Probability Plots of Effects. 8.3 Fractional Factorial Experiments in the 2k-p Series. 8.3.1 Introduction to Fractional Factorial Experiments, Confounding and Resolution. 8.3.2 Case Study Examples. 8.4 Taguchi Experimental Designs. 8.5 Exercises and Follow-Up Activities. 9 Evaluation of Measurement Processes. 9.1 Measurement Process Concepts. 9.1.1 Bias, Linearity, Repeatability and Reproducibility. 9.1.2 Inadequate Measurement Units. 9.2 Gauge Repeatability and Reproducibility Studies. 9.3 Comparison of Measurement Systems. 9.4 Attribute Scenarios. 9.5 Exercises and Follow-Up Activities. 10 Regression and Model Building. 10.1 Regression with a Single Predictor Variable. 10.2 Multiple Regression. 10.3 Response Surface Methods. 10.4 Categorical Data and Logistic Regression. 10.4.1 Tests of Association Using the Chi-Square Distribution. 10.4.2 Binary Logistic Regression. 10.5 Exercises and Follow-Up Activities. 11 Learning More and Further Minitab. 11.1 Learning More about Minitab and Obtaining Help. 11.1.1 Meet Minitab. 11.1.2 Help. 11.1.3 StatGuide. 11.1.4 Tutorials. 11.1.5 Assistant. 11.1.6 Glossary, Methods and Formulas. 11.1.7 Minitab on the Web and Knowledgebase/FAQ. 11.2 Macros. 11.2.1 Minitab Session Commands. 11.2.2 Global and Local Minitab Macros. 11.3 Further Features of Minitab. 11.4 Quality Companion. 11.5 Postscript. Appendix 1. Appendix 2. Appendix 3. Appendix 4. References. Index.

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Book SynopsisBayesian networks have found application in a number of fields, including risk analysis, consumer help desks, tissue pathology, pattern recognition, credit assessment, computer network diagnosis, and artificial intelligence. Bayesian Networks is a self-contained introduction to the theory and applications of Bayesian networks.Trade Review"It assumes only a basic knowledge of probability, statistics and mathematics and is well suited for classroom teaching . . . Each chapter of the book is concluded with short notes on the literature and a set of helpful exercises." (Mathematical Reviews, 2011) "Extensively tested in classroom teaching … .The authors clearly define all concepts and provide numerous examples and exercises." (Book News, December 2009)Table of ContentsPreface. 1 Graphical models and probabilistic reasoning. 1.1 Introduction. 1.2 Axioms of probability and basic notations. 1.3 The Bayes update of probability. 1.4 Inductive learning. 1.5 Interpretations of probability and Bayesian networks. 1.6 Learning as inference about parameters. 1.7 Bayesian statistical inference. 1.8 Tossing a thumb-tack. 1.9 Multinomial sampling and the Dirichlet integral. Notes. Exercises: Probabilistic theories of causality, Bayes’ rule, multinomial sampling and the Dirichlet density. 2 Conditional independence, graphs and d-separation. 2.1 Joint probabilities. 2.2 Conditional independence. 2.3 Directed acyclic graphs and d-separation. 2.4 The Bayes ball. 2.5 Potentials. 2.6 Bayesian networks. 2.7 Object oriented Bayesian networks. 2.8 d-Separation and conditional independence. 2.9 Markov models and Bayesian networks. 2.10 I-maps and Markov equivalence. Notes. Exercises: Conditional independence and d-separation. 3 Evidence, sufficiency and Monte Carlo methods. 3.1 Hard evidence. 3.2 Soft evidence and virtual evidence. 3.3 Queries in probabilistic inference. 3.4 Bucket elimination. 3.5 Bayesian sufficient statistics and prediction sufficiency. 3.6 Time variables. 3.7 A brief introduction to Markov chain Monte Carlo methods. 3.8 The one-dimensional discrete Metropolis algorithm. Notes. Exercises: Evidence, sufficiency and Monte Carlo methods. 4 Decomposable graphs and chain graphs. 4.1 Definitions and notations. 4.2 Decomposable graphs and triangulation of graphs. 4.3 Junction trees. 4.4 Markov equivalence. 4.5 Markov equivalence, the essential graph and chain graphs. Notes. Exercises: Decomposable graphs and chain graphs. 5 Learning the conditional probability potentials. 5.1 Initial illustration: maximum likelihood estimate for a fork connection. 5.2 The maximum likelihood estimator for multinomial sampling. 5.3 MLE for the parameters in a DAG: the general setting. 5.4 Updating, missing data, fractional updating. Notes. Exercises: Learning the conditional probability potentials. 6 Learning the graph structure. 6.1 Assigning a probability distribution to the graph structure. 6.2 Markov equivalence and consistency. 6.3 Reducing the size of the search. 6.4 Monte Carlo methods for locating the graph structure. 6.5 Women in mathematics. Notes. Exercises: Learning the graph structure. 7 Parameters and sensitivity. 7.1 Changing parameters in a network. 7.2 Measures of divergence between probability distributions. 7.3 The Chan-Darwiche distance measure. 7.4 Parameter changes to satisfy query constraints. 7.5 The sensitivity of queries to parameter changes. Notes. Exercises: Parameters and sensitivity. 8 Graphical models and exponential families. 8.1 Introduction to exponential families. 8.2 Standard examples of exponential families. 8.3 Graphical models and exponential families. 8.4 Noisy ‘or’ as an exponential family. 8.5 Properties of the log partition function. 8.6 Fenchel Legendre conjugate. 8.7 Kullback-Leibler divergence. 8.8 Mean field theory. 8.9 Conditional Gaussian distributions. Notes. Exercises: Graphical models and exponential families. 9 Causality and intervention calculus. 9.1 Introduction. 9.2 Conditioning by observation and by intervention. 9.3 The intervention calculus for a Bayesian network. 9.4 Properties of intervention calculus. 9.5 Transformations of probability. 9.6 A note on the order of ‘see’ and ‘do’ conditioning. 9.7 The ‘Sure Thing’ principle. 9.8 Back door criterion, confounding and identifiability. Notes. Exercises: Causality and intervention calculus. 10 The junction tree and probability updating. 10.1 Probability updating using a junction tree. 10.2 Potentials and the distributive law. 10.3 Elimination and domain graphs. 10.4 Factorization along an undirected graph. 10.5 Factorizing along a junction tree. 10.6 Local computation on junction trees. 10.7 Schedules. 10.8 Local and global consistency. 10.9 Message passing for conditional Gaussian distributions. 10.10 Using a junction tree with virtual evidence and soft evidence. Notes. Exercises: The junction tree and probability updating. 11 Factor graphs and the sum product algorithm. 11.1 Factorization and local potentials. 11.2 The sum product algorithm. 11.3 Detailed illustration of the algorithm. Notes. Exercise: Factor graphs and the sum product algorithm. References. Index.

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Book SynopsisComprehensive coverage of this growing area of research. Carefully introduces each algorithm with examples and in-depth discussion. Includes many applications to real-world problems, including engineering design and scheduling. Accessible to those with limited knowledge of multi-objective optimization and evolutionary algorithms.Table of ContentsForeword. Preface. Prologue. Multi-Objective Optimization. Classical Methods. Evolutionary Algorithms. Non-Elitist Multi-Objective Evolutionary Algorithms. Elitist Multi-Objective Evolutionary Algorithms. Constrained Multi-Objective Evolutionary Algorithms. Salient Issues of Multi-Objective Evolutionary Algorithms. Applications of Multi-Objective Evolutionary Algorithms. Epilogue. References. Index.

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Book SynopsisStochastic Processes for Insurance and Finance offers a thorough yet accessible reference for researchers and practitioners of insurance mathematics. Building on recent and rapid developments in applied probability, the authors describe in general terms models based on Markov processes, martingales and various types of point processes.Table of ContentsTable of Contents: Concepts from Insurance and Finance. Probability Distributions. Premiums and Ordering of Risks. Distributions of Aggregate Claim Amount. Risk Processes. Renewal Processes and Random Walks. Markov Chains. Continuous-Time Markov Models. Martingale Techniques I. Martingale Techniques II. Piecewise Deterministic Markov Processes. Point Processes. Diffusion Models. Distribution Tables. References. Index.

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Book Synopsis- It reveals the interrelationships between multiple variables and features of the underlying conditional independence. - It covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. - Many numerical examples and exercises with solutions are included.Table of ContentsIndependence and Interaction. Independence Graphs. Information Divergence. The Inverse Variance. Graphical Gaussian Models. Graphical Log-Linear Models. Model Selection. Methods for Sparse Tables. Regression and Graphical Chain Models. Models for Mixed Variables. Decompositions and Decomposability. Appendices. References. Author Index. Subject Index.

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Book Synopsis- Offers an accessible introduction to sensitivity analysis. - Covers all the latest research. - Illustrates concepts with numerous examples, applications and case studies.Table of ContentsWhat is Sensitivity Analysis. Hitchhiker's Guide to Sensitivity Analysis. METHODS. Designs of Experiments. Screening Methods. Local Methods. Sampling-Based Methods. Reliability Algorithms: FORM and SORM Methods. Variance-Based Methods. Managing the Tyranny of Parameters in Mathematical Modelling of Physical Systems. Bayesian Sensitivity Analysis. Graphical Methods. APPLICATIONS. Practical Experience in Applying Sensitivity and Uncertainty Analysis. Scenario and Parametric Sensitivity and Uncertainty Analysis in Nuclear Waste Disposal Risk Assessment: The Case of GESAMAC. Sensitivity Analysis for Signal Extraction in Economic Time Series. A Dataless Precalibration Analysis in Solid State Physics. Appplication of First-Order (FORM) and Second-Order (SORM) Reliability Methods: Analysis and Interpretation of Sensitivity Measures Related to Groundwater Pressure Decreases and Resulting Ground Subsidence. One-at-a-Time and Mini-Global Analyses for Characterizing Model Sensitivity in the Nonlinear Ozone Predictions from the US EPA Regional Acid Deposition Model (RADM). Comparing Different Sensitivity Analysis Methods on a Chemical Reactions Model. An Application of Sensitivity Analysis to Fish Population Dynamics. Global Sensitivity Analysis: A Quality Assurance Tool in Environmental Policy Modelling. CONCLUSIONS. Assuring the Quality of Models Designed for Predictive Tasks. Fortune and Future of Sensitivity Analysis. References. Appendix. Index.

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Book SynopsisThis is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining.Trade Review"The book is written in a language that is easily accessible to business users who are not fluent in statistical methods and who have no prior exposure to the data mining or customer segmentation domain . . . This book is poised to become a standard reference, and I unconditionally recommend it to anyone working in this field." (Computing Reviews, 23 June 2011) "This is an excellent book for any data miner or anybody involved in CRM. The text is clear and pictures are well done and funny which is rare enough to be mentioned. From basic to advanced topics, the book is a very pleasant journey inside data mining with a clear focus on customer segmentation. Really advised if you're not a fan of formulas." (Data Mining Research, 18 March 2011)Table of ContentsAcknowledgements. 1. Data Mining in CRM. The CRM Strategy. What Can Data Mining Do? The Data Mining Methodology. Data Mining and Business Domain Expertise. Summary. 2. An Overview of Data Mining Techniques. Supervised Modeling. Unsupervised Modeling Techniques. Machine Learning/Artificial Intelligence vs. Statistical Techniques. Summary. 3. Data Mining Techniques for Segmentation. Segmenting Customers with Data Mining Techniques. Principal Components Analysis. Clustering Techniques. Examining and Evaluating the Cluster Solution. Understanding the Clusters through Profiling. Selecting the Optimal Cluster Solution. Cluster Profiling and Scoring with Supervised Models. An Introduction to Decision Tree Models. Summary. 4. The Mining Data Mart. Designing the Mining Data Mart. The Time Frame Covered by the Mining Data Mart. The Mining Data Mart for Retail Banking. The Mining Data Mart for Mobile Telephony Consumer (Residential) Customers. The Mining Data Mart for Retailers. Summary. 5. Customer Segmentation. An Introduction to Customer Segmentation. Segmentation Types in Consumer Markets. Segmentation in Business Markets. A Guide for Behavioral Segmentation. Segmentation Management Strategy. A Guide for Value-Based Segmentation. Designing Differentiated Strategies for the Value Segments. Summary. 6. Segmentation Applications in Banking. Segmentation for Credit Card Holders. Segmentation in Retail Banking. The Marketing Process. Segmentation in Retail Banking; A Summary. 7. Segmentation Applications in Telecommunications. Mobile Telephony. The Fixed Telephony Case. Summary. 8. Segmentation for Retailers. Segmentation in the Retail Industry. The RFM Analysis. Grouping Customers According to the Products They Buy. Summary. Further Reading. Index.

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Book SynopsisBayesian Analysis of Stochastic Process Models provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making, and important applied models based on stochastic processes.Table of ContentsPreface PART ONE BASIC CONCEPTS AND TOOLS 1 Stochastic Processes 11 1.1 Introduction 11 1.2 Key Concepts in Stochastic Processes 11 1.3 Main Classes of Stochastic Processes 16 1.4 Inference, Prediction and Decision Making 21 1.5 Discussion 23 2 Bayesian Analysis 27 2.1 Introduction 27 2.2 Bayesian Statistics 28 2.3 Bayesian Decision Analysis 37 2.4 Bayesian Computation 39 2.5 Discussion 51 PART TWO MODELS 3 Discrete Time Markov Chains 61 3.1 Introduction 61 3.2 Important Markov Chain Models 62 3.3 Inference for First Order Chains 66 3.4 Special Topics 76 3.5 Case Study: Wind Directions at Gijon 87 3.6 Markov Decision Processes 94 3.7 Discussion 97 4 Continuous Time Markov Chains and Extensions 105 4.1 Introduction 105 4.2 Basic Setup and Results 106 4.3 Inference and Prediction for CTMCs 108 4.4 Case Study: Hardware Availability through CTMCs 112 4.5 Semi-Markovian Processes 118 4.6 Decision Making with Semi-Markovian Decision Processes 122 4.7 Discussion 128 5 Poisson Processes and Extensions 133 5.1 Introduction 133 5.2 Basics on Poisson Processes 134 5.3 Homogeneous Poisson Processes 138 5.4 Nonhomogeneous Poisson Processes 147 5.5 Compound Poisson Processes 153 5.6 Further Extensions of Poisson Processes 154 5.7 Case Study: Earthquake Occurrences 157 5.8 Discussion 162 6 Continuous Time Continuous Space Processes 169 6.1 Introduction 169 6.2 Gaussian Processes 170 6.3 Brownian Motion and Fractional Brownian Motion 174 6.4 Di®usions 181 6.5 Case Study: Prey-predator Systems 184 6.6 Discussion 190 PART THREE APPLICATIONS 7 Queueing Analysis 201 7.1 Introduction 201 7.2 Basic Queueing Concepts 201 7.3 The Main Queueing Models 204 7.4 Inference for Queueing Systems 208 7.5 Inference for M=M=1 Systems 209 7.6 Inference for Non Markovian Systems 220 7.7 Decision Problems in Queueing Systems 229 7.8 Case Study: Optimal Number of Beds in a Hospital 230 7.9 Discussion 235 8 Reliability 245 8.1 Introduction 245 8.2 Basic Reliability Concepts 246 8.3 Renewal Processes 249 8.4 Poisson Processes 251 8.5 Other Processes 259 8.6 Maintenance 262 8.7 Case Study: Gas Escapes 263 8.8 Discussion 271 9 Discrete Event Simulation 279 9.1 Introduction 279 9.2 Discrete Event Simulation Methods 280 9.3 A Bayesian View of DES 283 9.4 Case Study: A G=G=1 Queueing System 286 9.5 Bayesian Output Analysis 288 9.6 Simulation and Optimization 292 9.7 Discussion 294 10 Risk Analysis 301 10.1 Introduction 301 10.2 Risk Measures 302 10.3 Ruin Problems 316 10.4 Case Study: Ruin Probability Estimation 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index

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Book SynopsisThis practical text is an essential source of information for those wanting to know how to deal with the variability that exists in every engineering situation. Using typical engineering data, it presents the basic statistical methods that are relevant, in simple numerical terms.Trade Review"This book appeals to students in all areas of engineering and also managers concerned with the quality of manufactured products. Academic engineers can use this text to teach their students basic practical skills in quality management and statistical engineering, without getting involved in the complex mathematical theory of probability on which statistical science is dependent." (Zentralblatt MATH, 1 August 2013) "This is a timely text that helps to support the development of these important skills. Its no-nonsense and useful approach gives a flavour of the main statistical tools and techniques in basic language." (Quality World, December 2009) "It deserves to become a standard text to encourage the best in industrial practice." (Engineering & Technology, November 2009)Table of ContentsAbout the Author vii Foreword ix Preface xi Acknowledgements xiii 1 Nature of Variability 1 2 Basic Statistical Methods 9 2.1 Variance 9 2.2 Divisor ‘n’ or ‘n-1’? 11 2.3 Covariance and Correlation 13 2.4 Normal Distribution 14 2.5 Cumulative Frequency Distributions 18 2.6 Binomial Distribution 20 2.7 Poisson Distribution 25 2.8 Chi-squared Distribution 26 Bibliography 31 3 Production 33 3.1 Sampling Inspection 34 3.2 Control Charts 37 3.3 Cusum Charts 40 3.4 Significance Tests 43 3.5 Analysis of Variance 49 3.6 Linear Regression 52 Bibliography 57 4 Engineering Design 61 4.1 Variance Synthesis 61 4.2 Factors of Safety 68 4.3 Tolerances 69 4.4 The Future 71 Bibliography 72 5 Research and Development 75 5.1 Design of Experiments 76 5.2 Evolutionary Operation 95 5.3 Multiple Regression 96 5.4 More Statistical Methods 111 Bibliography 115 6 Background 119 6.1 Measurement 119 6.2 Statistical Computing 121 Bibliography 127 7 Quality Management 129 7.1 Quality Planning 129 7.2 Quality Organisation 135 7.3 Directing the Quality Function 137 7.4 Controlling the Quality Function 140 7.5 Statistical Engineering 141 Bibliography 142 8 Conclusion 145 Appendix A: Guidelines 147 Appendix B: Recommended Books 151 Appendix C: Periodicals 161 Appendix D: Supplementary Bibliography 165 Appendix E: Statistical Tables 171 Index 173

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Book SynopsisThis is a practical book on how to apply statistical methods successfully. The Authors have deliberately kept formulae to a minimum to enable the reader to concentrate on how to use the methods and to understand what the methods are for. Each method is introduced and used in a real situation from industry or research. Each chapter features situations based on the authors' experience and looks at statistical methods for analysing data and, where appropriate, discusses the assumptions of these methods. Key features: Provides a practical hands-on manual for workplace applications. Introduces a broad range of statistical methods from confidence intervals to trend analysis. Combines realistic case studies and examples with a practical approach to statistical analysis. Features examples drawn from a wide range of industries including chemicals, petrochemicals, nuclear power, food and pharmaceuticals. Includes a supporting Trade Review"Overall, the book could be a clear introduction to a set of useful tools either in self study or used as an aid for instruction for those with no previous exposure." (The American Statistician, 1 February 2011) Table of ContentsPreface. 1 Samples and populations. Introduction. What a lottery! No can do. Nobody is listening to me. How clean is my river? Discussion. 2 What is the true mean? Introduction. Presenting data. Averages. Measures of variability. Relative standard deviation . Degrees of freedom. Confidence interval for the population mean. Sample sizes. How much moisture is in the raw material? Problems. 3 Exploratory data analysis. Introduction. Histograms: is the process capable of meeting specifications? Box plots: how long before the lights go out? The box plot in practice. Problems. 4 Significance testing. Introduction. The one-sample t -test. The significance testing procedure. Confidence intervals as an alternative to significance testing. Confidence interval for the population standard deviation. F-test for ratio of standard deviations. Problems. 5 The normal distribution. Introduction. Properties of the normal distribution. Example. Setting the process mean. Checking for normality. Uses of the normal distribution. Problems. 6 Tolerance intervals. Introduction. Example. Confidence intervals and tolerance intervals. 7 Outliers. Introduction. Grubbs’ test. Warning. 8 Significance tests for comparing two means. Introduction. Example: watching paint lose its gloss. The two-sample t -test for independent samples. An alternative approach: a confidence intervals for the difference between population means. Sample size to estimate the difference between two means. A production example. Confidence intervals for the difference between the two suppliers. Sample size to estimate the difference between two means. Conclusions. Problems. 9 Significance tests for comparing paired measurements. Introduction. Comparing two fabrics. The wrong way. The paired sample t -test. Presenting the results of significance tests. One-sided significance tests. Problems. 10 Regression and correlation. Introduction. Obtaining the best straight line. Confidence intervals for the regression statistics. Extrapolation of the regression line. Correlation coefficient. Is there a significant relationship between the variables? How good a fit is the line to the data? Assumptions. Problems. 11 The binomial distribution. Introduction. Example. An exact binomial test. A quality assurance example. What is the effect of the batch size? Problems. 12 The Poisson distribution. Introduction. Fitting a Poisson distribution. Are the defects random? The Poisson distribution. Poisson dispersion test. Confidence intervals for a Poisson count. A significance test for two Poisson counts. How many black specks are in the batch? How many pathogens are there in the batch? Problems. 13 The chi-squared test for contingency tables. Introduction. Two-sample test for percentages. Comparing several percentages. Where are the differences? Assumptions. Problems. 14 Non-parametric statistics. Introduction. Descriptive statistics. A test for two independent samples: Wilcoxon–Mann–Whitney test. A test for paired data: Wilcoxon matched-pairs sign test. What type of data can be used? Example: cracking shoes. Problems. 15 Analysis of variance: Components of variability. Introduction. Overall variability. Analysis of variance. A practical example. Terminology. Calculations. Significance test. Variation less than chance? When should the above methods not be used? Between- and within-batch variability. How many batches and how many prawns should be sampled? Problems. 16 Cusum analysis for detecting process changes. Introduction. Analysing past data. Intensity. Localised standard deviation. Significance test. Yield. Conclusions from the analysis. Problem. 17 Rounding of results. Introduction. Choosing the rounding scale. Reporting purposes: deciding the amount of rounding. Reporting purposes: rounding of means and standard deviations. Recording the original data and using means and standard deviations in statistical analysis. References. Solutions to Problems. Statistical Tables. Index.

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Book Synopsis* Presents the latest developments in Monte Carlo research. * Provides a toolkit for simulating complex systems using MCMC. * Introduces a wide range of algorithms including Gibbs sampler, Metropolis-Hastings and an overview of sequential Monte Carlo algorithms.Trade Review"The book is suitable as a textbook for one-semester courses on Monte Carlo methods, offered at the advance postgraduate levels." (Mathematical Reviews, 1 December 2012) "Researchers working in the field of applied statistics will profit from this easy-to-access presentation. Further illustration is done by discussing interesting examples and relevant applications. The valuable reference list includes technical reports which are hard to and by searching in public data bases." (Zentralblatt MATH, 2011) "This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial." (Breitbart.com: Business Wire , 1 February 2011) "The Markov Chain Monte Carlo method has now become the dominant methodology for solving many classes of computational problems in science and technology." (SciTech Book News, December 2010)Table of ContentsPreface xiii Acknowledgments xvii Publisher’s Acknowledgments xix 1 Bayesian Inference and Markov Chain Monte Carlo 1 1.1 Bayes 1 1.1.1 Specification of Bayesian Models 2 1.1.2 The Jeffreys Priors and Beyond 2 1.2 Bayes Output 4 1.2.1 Credible Intervals and Regions 4 1.2.2 Hypothesis Testing: Bayes Factors 5 1.3 Monte Carlo Integration 8 1.3.1 The Problem 8 1.3.2 Monte Carlo Approximation 9 1.3.3 Monte Carlo via Importance Sampling 9 1.4 Random Variable Generation 10 1.4.1 Direct or Transformation Methods 1 1.4.2 Acceptance-Rejection Methods 11 1.4.3 The Ratio-of-Uniforms Method and Beyond 14 1.4.4 Adaptive Rejection Sampling 18 1.4.5 Perfect Sampling 18 1.5 Markov Chain Monte Carlo 18 1.5.1 Markov Chains 18 1.5.2 Convergence Results 20 1.5.3 Convergence Diagnostics 23 Exercises 24 2 The Gibbs Sampler 27 2.1 The Gibbs Sampler 27 2.2 Data Augmentation 30 2.3 Implementation Strategies and Acceleration Methods 33 2.3.1 Blocking and Collapsing 33 2.3.2 Hierarchical Centering and Reparameterization 34 2.3.3 Parameter Expansion for Data Augmentation 35 2.3.4 Alternating Subspace-Spanning Resampling 43 2.4 Applications 45 2.4.1 The Student-t Model 45 2.4.2 Robit Regression or Binary Regression with the Student-t Link 47 2.4.3 Linear Regression with Interval-Censored Responses 50 Exercises 54 Appendix 2A: The EM and PX-EM Algorithms 56 3 The Metropolis-Hastings Algorithm 59 3.1 The Metropolis-Hastings Algorithm 59 3.1.1 Independence Sampler 62 3.1.2 Random Walk Chains 63 3.1.3 Problems with Metropolis-Hastings Simulations 63 3.2 Variants of the Metropolis-Hastings Algorithm 65 3.2.1 The Hit-and-Run Algorithm. 65 3.2.2 The Langevin Algorithm 65 3.2.3 The Multiple-Try MH Algorithm 66 3.3 Reversible Jump MCMC Algorithm for Bayesian Model Selection Problems 67 3.3.1 Reversible Jump MCMC Algorithm 67 3.3.2 Change-Point Identification 70 3.4 Metropolis-Within-Gibbs Sampler for ChIP-chip Data Analysis 75 3.4.1 Metropolis-Within-Gibbs Sampler 75 3.4.2 Bayesian Analysis for ChIP-chip Data 76 Exercises 83 4 Auxiliary Variable MCMC Methods 85 4.1 Simulated Annealing 86 4.2 Simulated Tempering 88 4.3 The Slice Sampler 90 4.4 The Swendsen-Wang Algorithm 91 4.5 The Wolff Algorithm 93 4.6 The Mo/ller Algorithm 95 4.7 The Exchange Algorithm 97 4.8 The Double MH Sampler 98 4.8.1 Spatial Autologistic Models 99 4.9 Monte Carlo MH Sampler 103 4.9.1 Monte Carlo MH Algorithm 103 4.9.2 Convergence 107 4.9.3 Spatial Autologistic Models (Revisited) 110 4.9.4 Marginal Inference 111 4.10 Applications 113 4.10.1 Autonormal Models 114 4.10.2 Social Networks 116 Exercises 121 5 Population-Based MCMC Methods 123 5.1 Adaptive Direction Sampling 124 5.2 Conjugate Gradient Monte Carlo 125 5.3 Sample Metropolis-Hastings Algorithm 126 5.4 Parallel Tempering 127 5.5 Evolutionary Monte Carlo 128 5.5.1 Evolutionary Monte Carlo in Binary-Coded Space 129 5.5.2 Evolutionary Monte Carlo in Continuous Space 132 5.5.3 Implementation Issues 133 5.5.4 Two Illustrative Examples 134 5.5.5 Discussion 139 5.6 Sequential Parallel Tempering for Simulation of High Dimensional Systems 140 5.6.1 Build-up Ladder Construction 141 5.6.2 Sequential Parallel Tempering 142 5.6.3 An Illustrative Example: the Witch’s Hat Distribution 142 5.6.4 Discussion 145 5.7 Equi-Energy Sampler 146 5.8 Applications 148 5.8.1 Bayesian Curve Fitting 148 5.8.2 Protein Folding Simulations: 2D HP Model 153 5.8.3 Bayesian Neural Networks for Nonlinear Time Series Forecasting 156 Exercises 162 Appendix 5A: Protein Sequences for 2D HP Models 163 6 Dynamic Weighting 165 6.1 Dynamic Weighting 165 6.1.1 The IWIW Principle 165 6.1.2 Tempering Dynamic Weighting Algorithm 167 6.1.3 Dynamic Weighting in Optimization 171 6.2 Dynamically Weighted Importance Sampling 173 6.2.1 The Basic Idea 173 6.2.2 A Theory of DWIS 174 6.2.3 Some IWIWp Transition Rules 176 6.2.4 Two DWIS Schemes 179 6.2.5 Weight Behavior Analysis 180 6.2.6 A Numerical Example 183 6.3 Monte Carlo Dynamically Weighted Importance Sampling 185 6.3.1 Sampling from Distributions with Intractable Normalizing Constants 185 6.3.2 Monte Carlo Dynamically Weighted Importance Sampling 186 6.3.3 Bayesian Analysis for Spatial Autologistic Models 191 6.4 Sequentially Dynamically Weighted Importance Sampling 195 Exercises 197 7 Stochastic Approximation Monte Carlo 199 7.1 Multicanonical Monte Carlo 200 7.2 1/k-Ensemble Sampling 202 7.3 The Wang-Landau Algorithm 204 7.4 Stochastic Approximation Monte Carlo 207 7.5 Applications of Stochastic Approximation Monte Carlo 218 7.5.1 Efficient p-Value Evaluation for Resampling-Based Tests 218 7.5.2 Bayesian Phylogeny Inference 222 7.5.3 Bayesian Network Learning 227 7.6 Variants of Stochastic Approximation Monte Carlo 233 7.6.1 Smoothing SAMC for Model Selection Problems 233 7.6.2 Continuous SAMC for Marginal Density Estimation 239 7.6.3 Annealing SAMC for Global Optimization 244 7.7 Theory of Stochastic Approximation Monte Carlo 253 7.7.1 Convergence 253 7.7.2 Convergence Rate 267 7.7.3 Ergodicity and its IWIW Property 271 7.8 Trajectory Averaging: Toward the Optimal Convergence Rate 275 7.8.1 Trajectory Averaging for a SAMCMC Algorithm 277 7.8.2 Trajectory Averaging for SAMC 279 7.8.3 Proof of Theorems 7.8.2 and 7.8.3 281 Exercises 296 Appendix 7A: Test Functions for Global Optimization 298 8 Markov Chain Monte Carlo with Adaptive Proposals 305 8.1 Stochastic Approximation-Based Adaptive Algorithms 306 8.1.1 Ergodicity and Weak Law of Large Numbers 307 8.1.2 Adaptive Metropolis Algorithms 309 8.2 Adaptive Independent Metropolis-Hastings Algorithms 312 8.3 Regeneration-Based Adaptive Algorithms 315 8.3.1 Identification of Regeneration Times 315 8.3.2 Proposal Adaptation at Regeneration Times 317 8.4 Population-Based Adaptive Algorithms 317 8.4.1 ADS, EMC, NKC and More 317 8.4.2 Adaptive EMC 318 8.4.3 Application to Sensor Placement Problems 323 Exercises 324 References 327 Index 353

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Book SynopsisThere is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this Table of ContentsPreface. List of Contributors. PART I FUNDAMENTALS. Section 1 Knowledge-Driven Approaches. 1 Knowledge-based bioinformatics (Eric Karl Neumann). 1.1 Introduction. 1.2 Formal reasoning for bioinformatics. 1.3 Knowledge representations. 1.4 Collecting explicit knowledge. 1.5 Representing common knowledge. 1.6 Capturing novel knowledge. 1.7 Knowledge discovery applications. 1.8 Semantic harmonization: the power and limitation of ontologies. 1.9 Text mining and extraction. 1.10 Gene expression. 1.11 Pathways and mechanistic knowledge. 1.12 Genotypes and phenotypes. 1.13 The Web's role in knowledge mining. 1.14 New frontiers. 1.15 References. 2 Knowledge-driven approaches to genome-scale analysis (Hannah Tipney and Lawrence Hunter). 2.1 Fundamentals. 2.2 Challenges in knowledge-driven approaches. 2.3 Current knowledge-based bioinformatics tools. 2.4 3R systems: reading, reasoning and reporting the way towards biomedical discovery. 2.5 The Hanalyzer: a proof of 3R concept. 2.6 Acknowledgements. 2.7 References. 3 Technologies and best practices for building bio-ontologies (Mikel Egaña Aranguren, Robert Stevens, Erick Antezana, Jesualdo Tomás Fernández-Breis, Martin Kuiper, and Vladimir Mironov). 3.1 Introduction. 3.2 Knowledge representation languages and tools for building bio-ontologies. 3.3 Best practices for building bio-ontologies. 3.4 Conclusion. 3.5 Acknowledgements. 3.6 References. 4 Design, implementation and updating of knowledge bases (Sarah Hunter, Rolf Apweiler, and Maria Jesus Martin). 4.1 Introduction. 4.2 Sources of data in bioinformatics knowledge bases. 4.3 Design of knowledge bases. 4.4 Implementation of knowledge bases. 4.5 Updating of knowledge bases. 4.6 Conclusions. 4.7 References. Section 2 Data-Analysis Approaches. 5 Classical statistical learning in bioinformatics (Mark Reimers). 5.1 Introduction. 5.2 Significance testing. 5.3 Exploratory analysis. 5.4 Classification and prediction. 5.5 References. 6 Bayesian methods in genomics and proteomics studies (Ning Sun and Hongyu Zhao). 6.1 Introduction. 6.2 Bayes theorem and some simple applications. 6.3 Inference of population structure from genetic marker data. 6.4 Inference of protein binding motifs from sequence data. 6.5 Inference of transcriptional regulatory networks from joint analysis of protein–DNA binding data and gene expression data. 6.6 Inference of protein and domain interactions from yeast two-hybrid data. 6.7 Conclusions. 6.8 Acknowledgements. 6.9 References. 7 Automatic text analysis for bioinformatics knowledge discovery (Dietrich Rebholz-Schuhmann and Jung-jae Kim). 7.1 Introduction. 7.2 Information needs for biomedical text mining. 7.3 Principles of text mining. 7.4 Development issues. 7.5 Success stories. 7.6 Conclusion. 7.7 References. PART II APPLICATIONS. Section 3 Gene and Protein Information. 8 Fundamentals of gene ontology functional annotation (Varsha K. Khodiyar, Emily C. Dimmer, Rachael P. Huntley, and Ruth C. Lovering). 8.1 Introduction. 8.2 Gene Ontology (GO). 8.3 Comparative genomics and electronic protein annotation. 8.4 Community annotation. 8.5 Limitations. 8.6 Accessing GO annotations. 8.7 Conclusions. 8.8 References. 9 Methods for improving genome annotation (Jonathan Mudge and Jennifer Harrow). 9.1 The basis of gene annotation. 9.2 The impact of next generation sequencing on genome annotation. 9.3 References. 10 Sequences from prokaryotic, eukaryotic, and viral genomes available clustered according to phylotype on a Self-Organizing Map (Takashi Abe, Shigehiko Kanaya, and Toshimichi Ikemura). 10.1 Introduction. 10.2 Batch-learning SOM (BLSOM) adapted for genome informatics. 10.3 Genome sequence analyses using BLSOM. 10.4 Conclusions and discussion. 10.5 References. Section 4 Biomolecular Relationships and Meta-Relationships. 11 Molecular network analysis and applications (Minlu Zhang, Jingyuan Deng, Chunsheng V. Fang, Xiao Zhang, and Long Jason Lu). 11.1 Introduction. 11.2 Topology analysis and applications. 11.3 Network motif analysis. 11.4 Network modular analysis and applications. 11.5 Network comparison. 11.6 Network analysis software and tools. 11.7 Summary. 11.8 Acknowledgement. 11.9 References. 12 Biological pathway analysis: an overview of Reactome and other integrative pathway knowledge bases (Robin A. Haw, Marc E. Gillespie, and Michael A. Caudy). 12.1 Biological pathway analysis and pathway knowledge bases. 12.2 Overview of high-throughput data capture technologies and data repositories. 12.3 Brief review of selected pathway knowledge bases. 12.4 How does information get into pathway knowledge bases? 12.5 Introduction to data exchange languages. 12.6 Visualization tools. 12.7 Use case: pathway analysis in Reactome using statistical analysis of high-throughput data sets. 12.8 Discussion: challenges and future directions of pathway knowledge bases. 12.9 References. 13 Methods and challenges of identifying biomolecular relationships and networks associated with complex diseases/phenotypes, and their application to drug treatments (Mie Rizig). 13.1 Complex traits: clinical phenomenology and molecular background. 13.2 Why it is challenging to infer relationships between genes and phenotypes in complex traits? 317 13.3 Bottom-up or top-down: which approach is more useful in delineating complex traits key drivers? 13.4 High-throughput technologies and their applications in complex traits genetics. 13.5 Integrative systems biology: a comprehensive approach to mining high-throughput data. 13.6 Methods applying systems biology approach in the identification of functional relationships from gene expression data. 13.7 Advantages of networks exploration in molecular biology and drug discovery. 13.8 Practical examples of applying systems biology approaches and network exploration in the identification of functional modules and disease-causing genes in complex phenotypes/diseases. 13.9 Challenges and future directions. 13.10 References. Trends and conclusion. Index.

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Book SynopsisThis book aims to get the electrical and electronic engineering student well-versed in the machinery of probability theory. It steers clear of getting into application areas any more than is needed to get the reader comfortable with the mathematics and connecting it to models of practical situations.Trade Review"After reading some introductory material on conventions and notions, it is possible to use separate chapters as introductions to various ideas. This is how readers should use this book." (Computing Reviews, 1 October 2011)Table of ContentsPreface. Introduction. Part I. The Basic Model. Part I Introduction. Section 1. Dealing with ‘Real-World’ Problems. Section 2. The Probabilistic Experiment. Section 3. Outcome. Section 4. Events. Section 5. The Connection to the Mathematical World. Section 6. Elements and Sets. Section 7. Classes of Sets. Section 8. Elementary Set Operations. Section 9. Additional Set Operations. Section 10. Functions. Section 11. The Size of a Set. Section 12. Multiple and Infinite Set Operations. Section 13. More About Additive Classes. Section 14. Additive Set Functions. Section 15. More about Probabilistic Experiments. Section 16. The Probability Function. Section 17. Probability Space. Section 18. Simple Probability Arithmetic. Part I Summary. Part II. The Approach to Elementary Probability Problems. Part II. Introduction. Section 19. About Probability Problems. Section 20. Equally Likely Possible Outcomes. Section 21. Conditional Probability. Section 22. Conditional Probability Distributions. Section 23. Independent Events. Section 24. Classes of Independent Events. Section 25. Possible Outcomes Represented as Ordered k-Tuples. Section 26. Product Experiments and Product Spaces. Section 27. Product Probability Spaces. Section 28. Dependence Between the Components in an Ordered k-Tuple. Section 29. Multiple Observations Without Regard to Order. Section 30. Unordered Sampling with Replacement. Section 31. More Complicated Discrete Probability Problems. Section 32. Uncertainty and Randomness. Section 33. Fuzziness. Part II Summary. Part III. Introduction to Random Variables. Part III. Introduction. Section 34. Numerical-Valued Outcomes. Section 35. The Binomial Distribution. Section 36. The Real Numbers. Section 37. General Definition of a Random Variable. Section 38. The Cumulative Distribution Function. Section 39. The Probability Density Function. Section 40. The Gaussian Distribution. Section 41. Two Discrete Random Variables. Section 42. Two Arbitrary Random Variables. Section 43. Two-Dimensional Distribution Functions. Section 44. Two-Dimensional Density Functions. Section 45. Two Statistically Independent Random Variables. Section 46. Two Statistically Independent Random Variables-Absolutely Continuous Case. Part III Summary. Part IV. Transformations and Multiple Random Variables. Part IV Introduction. Section 47. Transformation of a Random Variable. Section 48. Transformation of a Two-Dimensional Random Variable. Section 49. The Sum of Two Discrete Random Variables. Section 50. The Sum of Two Arbitrary Random Variables. Section 51. n-Dimensional Random Variables. Section 52. Absolutely Continuous n-Dimensional R. V.’s. Section 53. Coordinate Transformations. Section 54. Rotations and the Bivariate Gaussian Distribution. Section 55. Several Statistically Independent Random Variables. Section 56. Singular Distributions in One Dimension. Section 57. Conditional Induced Distribution, Given an Event. Section 58. Resolving a Distribution into Components of Pure Type. Section 59. Conditional Distribution Given the Value of a Random Variable. Section 60. Random Occurrences in Time. Part IV Summary. Part V. Parameters for Describing Random Variables and Induced Distributions. Section 61. Some Properties of a Random Variable. Section 62. Higher Moments. Section 63. Expectation of a Function of a Random Variable. Section 64. The Variance of a Function of a Random Variable. Section 65. Bounds on the Induced Distribution. Section 66. Test Sampling. Section 67. Conditional Expectation with Respect to an Event. Section 68. Covariance and Correlation Coefficient. Section 69. The Correlation Coefficient as Parameter in a Joint Distribution. Section 70. More General Kinds of Dependence Between Random Variables. Section 71. The Covariance Matrix. Section 72. Random Variables as the Elements of a Vector Space. Section 73. Estimation. Section 74. The Stieltjes Integral. Part V Summary. Part VI. Further Topics in Random Variables. Part VI Introduction. Section 75. Complex Random Variables. Section 76. The Characteristic Function. Section 77. Characteristic Function of a Transformed Random Variable. Section 78. Characteristic Function of a Multidimensional Random Variable. Section 79. The Generating Function. Section 80. Several Jointly Gaussian Random Variables. Section 81. Spherically Symmetric Vector Random Variables. Section 82. Entropy Associated with Random Variables. Section 83. Copulas. Section 84. Sequences of Random Variables. Section 85. Convergent Sequences and Laws of Large Numbers. Section 86. Convergence of Probability Distributions and the Central Limit Theorem. Part VI Summary. Appendices. Notation and Abbreviations. References. Subject Index.

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Book SynopsisThroughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models. Key Features: Provides a clear introduction and a comprehensive account of multilevel models. New methodological developments and applications are explored. Written by a leading Trade Review"This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial. See the review of the third edition." (Zentralblatt MATH, 1 December 2013) "This book would also serve as an outstanding general reference on multilevel models, since it offers concise and easy to follow descriptions of the various multilevel models and their applications, in addition to the references on which this work is based. I really enjoyed reading this book, and am sure that others will have a similar pleasurable experience." (Journal of Biopharmaceutical Statistics (JBS), 2012) Table of ContentsContents Dedication Preface Acknowledgements Notation A general classification notation and diagram Glossary Chapter 1 An introduction to multilevel models 1.1 Hierarchically structured data 1.2 School effectiveness 1.3 Sample survey methods 1.4 Repeated measures data 1.5 Event history and survival models 1.6 Discrete response data 1.7 Multivariate models 1.8 Nonlinear models 1.9 Measurement errors 1.10 Cross classifications and multiple membership structures. 1.11 Factor analysis and structural equation models 1.12 Levels of aggregation and ecological fallacies 1.13 Causality 1.14 The latent normal transformation and missing data 1.15 Other texts 1.16 A caveat Chapter 2 The 2-level model 2.1 Introduction 2.2 The 2-level model 2.3 Parameter estimation 2.4 Maximum likelihood estimation using Iterative Generalised Least Squares (IGLS) 2.5 Marginal models and Generalized Estimating Equations (GEE) 2.6 Residuals 2.7 The adequacy of Ordinary Least Squares estimates. 2.8 A 2-level example using longitudinal educational achievement data 2.9 General model diagnostics 2.10 Higher level explanatory variables and compositional effects 2.11 Transforming to normality 2.12 Hypothesis testing and confidence intervals 2.13 Bayesian estimation using Markov Chain Monte Carlo (MCMC) 2.14 Data augmentation Appendix 2.1 The general structure and maximum likelihood estimation for a multilevel model Appendix 2.2 Multilevel residuals estimation Appendix 2.3 Estimation using profile and extended likelihood Appendix 2.4 The EM algorithm Appendix 2.5 MCMC sampling Chapter 3. Three level models and more complex hierarchical structures. 3.1 Complex variance structures 3.2 A 3-level complex variation model example. 3.3 Parameter Constraints 3.4 Weighting units 3.5 Robust (Sandwich) Estimators and Jacknifing 3.6 The bootstrap 3.7 Aggregate level analyses 3.8 Meta analysis 3.9 Design issues Chapter 4. Multilevel Models for discrete response data 4.1 Generalised linear models 4.2 Proportions as responses 4.3 Examples 4.4 Models for multiple response categories 4.5 Models for counts 4.6 Mixed discrete - continuous response models 4.7 A latent normal model for binary responses 4.8 Partitioning variation in discrete response models Appendix 4.1. Generalised linear model estimation Appendix 4.2 Maximum likelihood estimation for generalised linear models Appendix 4.3 MCMC estimation for generalised linear models Appendix 4.4. Bootstrap estimation for generalised linear models Chapter 5. Models for repeated measures data 5.1 Repeated measures data 5.2 A 2-level repeated measures model 5.3 A polynomial model example for adolescent growth and the prediction of adult height 5.4 Modelling an autocorrelation structure at level 1. 5.5 A growth model with autocorrelated residuals 5.6 Multivariate repeated measures models 5.7 Scaling across time 5.8 Cross-over designs 5.9 Missing data 5.10 Longitudinal discrete response data Chapter 6. Multivariate multilevel data 6.1 Introduction 6.2 The basic 2-level multivariate model 6.3 Rotation Designs 6.4 A rotation design example using Science test scores 6.5 Informative response selection: subject choice in examinations 6.6 Multivariate structures at higher levels and future predictions 6.7 Multivariate responses at several levels 6.8 Principal Components analysis Appendix 6.1 MCMC algorithm for a multivariate normal response model with constraints Chapter 7. Latent normal models for multivariate data 7.1 The normal multilevel multivariate model 7.2 Sampling binary responses 7.3 Sampling ordered categorical responses 7.4 Sampling unordered categorical responses 7.5 Sampling count data 7.6 Sampling continuous non-normal data 7.7 Sampling the level 1 and level 2 covariance matrices 7.8 Model fit 7.9 Partially ordered data 7.10 Hybrid normal/ordered variables 7.11 Discussion Chapter 8. Multilevel factor analysis, structural equation and mixture models 8.1 A 2-stage 2-level factor model 8.2 A general multilevel factor model 8.3 MCMC estimation for the factor model 8.4 Structural equation models 8.5 Discrete response multilevel structural equation models 8.6 More complex hierarchical latent variable models 8.7 Multilevel mixture models Chapter 9. Nonlinear multilevel models 9.1 Introduction 9.2 Nonlinear functions of linear components 9.3 Estimating population means 9.4 Nonlinear functions for variances and covariances 9.5 Examples of nonlinear growth and nonlinear level 1 variance Appendix 9.1 Nonlinear model estimation Chapter 10. Multilevel modelling in sample surveys 10.1 Sample survey structures 10.2 Population structures 10.3 Small area estimation Chapter 11 Multilevel event history and survival models 11.1 Introduction 11.2 Censoring 11.3 Hazard and survival funtions 11.4 Parametric proportional hazard models 11.5 The semiparametric Cox model 11.6 Tied observations 11.7 Repeated events proportional hazard models 11.8 Example using birth interval data 11.9 Log duration models 11.10 Examples with birth interval data and children’s activity episodes 11.11 The grouped discrete time hazards model 11.12 Discrete time latent normal event history models Chapter 12. Cross classified data structures 12.1 Random cross classifications 12.2 A basic cross classified model 12.3 Examination results for a cross classification of schools 12.4 Interactions in cross classifications 12.5 Cross classifications with one unit per cell 12.6 Multivariate cross classified models 12.7 A general notation for cross classifications 12.8 MCMC estimation in cross classified models Appendix 12.1 IGLS Estimation for cross classified data. Chapter 13 Multiple membership models 13.1 Multiple membership structures 13.2 Notation and classifications for multiple membership structures 13.3 An example of salmonella infection 13.4 A repeated measures multiple membership model 13.5 Individuals as higher level units 13.5.1 Example of research grant awards 13.6 Spatial models 13.7 Missing identification models Appendix 13.1 MCMC estimation for multiple membership models. Chapter 14 Measurement errors in multilevel models 14.1 A basic measurement error model 14.2 Moment based estimators 14.3 A 2-level example with measurement error at both levels. 14.4 Multivariate responses 14.5 Nonlinear models 14.6 Measurement errors for discrete explanatory variables 14.7 MCMC estimation for measurement error models Appendix 14.1 Measurement error estimation 14.2 MCMC estimation for measurement error models Chapter 15. Smoothing models for multilevel data. 15.1 Introduction 15.2. Smoothing estimators 15.3 Smoothing splines 15.4 Semi parametric smoothing models 15.5 Multilevel smoothing models 15.6 General multilevel semi-parametric smoothing models 15.7 Generalised linear models 15.8 An example Fixed Random 15.9 Conclusions Chapter 16. Missing data, partially observed data and multiple imputation 16.1 Creating a completed data set 16.2 Joint modelling for missing data 16.3 A two level model with responses of different types at both levels. 16.4 Multiple imputation 16.5 A simulation example of multiple imputation for missing data 16.6 Longitudinal data with attrition 16.7 Partially known data values 16.8 Conclusions Chapter 17 Multilevel models with correlated random effects 17.1 Non-independence of level 2 residuals 17.2 MCMC estimation for non-independent level 2 residuals 17.3 Adaptive proposal distributions in MCMC estimation 17.4 MCMC estimation for non-independent level 1 residuals 17.5 Modelling the level 1 variance as a function of explanatory variables with random effects 17.6 Discrete responses with correlated random effects 17.7 Calculating the DIC statistic 17.8 A growth data set 17.9 Conclusions Chapter 18. Software for multilevel modelling References Author index Subject index

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Book SynopsisText Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.Trade Review"It is extremely useful for practitioners and students in computer science, natural language processing, bioinformatics and engineering who wish to use text mining techniques." (Journal of Information Retrieval, 1 April 2011)Table of ContentsList of Contributors. Preface. PART I TEXT EXTRACTION, CLASSIFICATION, AND CLUSTERING. 1 Automatic keyword extraction from individual documents. 1.1 Introduction. 1.2 Rapid automatic keyword extraction. 1.3 Benchmark evaluation. 1.4 Stoplist generation. 1.5 Evaluation on news articles. 1.6 Summary. 1.7 Acknowledgements. 2 Algebraic techniques for multilingual document clustering. 2.1 Introduction. 2.2 Background. 2.3 Experimental setup. 2.4 Multilingual LSA. 2.5 Tucker1 method. 2.6 PARAFAC2 method. 2.7 LSA with term alignments. 2.8 Latent morpho-semantic analysis (LMSA). 2.9 LMSA with term alignments. 2.10 Discussion of results and techniques. 2.11 Acknowledgements. 3 Content-based spam email classification using machine-learning algorithms. 3.1 Introduction. 3.2 Machine-learning algorithms. 3.3 Data preprocessing. 3.4 Evaluation of email classification. 3.5 Experiments. 3.6 Characteristics of classifiers. 3.7 Concluding remarks. 3.8 Acknowledgements. 4 Utilizing nonnegative matrix factorization for email classification problems. 4.1 Introduction. 4.2 Background. 4.3 NMF initialization based on feature ranking. 4.4 NMF-based classification methods. 4.5 Conclusions. 4.6 Acknowledgements. 5 Constrained clustering with k-means type algorithms. 5.1 Introduction. 5.2 Notations and classical k-means. 5.3 Constrained k-means with Bregman divergences. 5.4 Constrained smoka type clustering. 5.5 Constrained spherical k-means. 5.6 Numerical experiments. 5.7 Conclusion. PART II ANOMALY AND TREND DETECTION. 6 Survey of text visualization techniques. 6.1 Visualization in text analysis. 6.2 Tag clouds. 6.3 Authorship and change tracking. 6.4 Data exploration and the search for novel patterns. 6.5 Sentiment tracking. 6.6 Visual analytics and FutureLens. 6.7 Scenario discovery. 6.8 Earlier prototype. 6.9 Features of FutureLens. 6.10 Scenario discovery example: bioterrorism. 6.11 Scenario discovery example: drug trafficking. 6.12 Future work. 7 Adaptive threshold setting for novelty mining. 7.1 Introduction. 7.2 Adaptive threshold setting in novelty mining. 7.3 Experimental study. 7.4 Conclusion. 8 Text mining and cybercrime. 8.1 Introduction. 8.2 Current research in Internet predation and cyberbullying. 8.3 Commercial software for monitoring chat. 8.4 Conclusions and future directions. 8.5 Acknowledgements. PART III TEXT STREAMS. 9 Events and trends in text streams. 9.1 Introduction. 9.2 Text streams. 9.3 Feature extraction and data reduction. 9.4 Event detection. 9.5 Trend detection. 9.6 Event and trend descriptions. 9.7 Discussion. 9.8 Summary. 9.9 Acknowledgements. 10 Embedding semantics in LDA topic models. 10.1 Introduction. 10.2 Background. 10.3 Latent Dirichlet allocation. 10.4 Embedding external semantics from Wikipedia. 10.5 Data-driven semantic embedding. 10.6 Related work. 10.7 Conclusion and future work. References. Index.

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Book Synopsis* Presents a comprehensive guide to clustering techniques. * Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies * Includes a new section on how to use R for cluster analysis.Table of ContentsPreface. Acknowledgement. 1 An Introduction to classification and clustering. 1.1 Introduction. 1.2 Reasons for classifying. 1.3 Numerical methods of classification – cluster analysis. 1.4 What is a cluster? 1.5 Examples of the use of clustering. 1.5.1 Market research. 1.5.2 Astronomy. 1.5.3 Psychiatry. 1.5.4 Weather classification. 1.5.5 Archaeology. 1.5.6 Bioinformatics and genetics. 1.6 Summary. 2 Detecting clusters graphically. 2.1 Introduction. 2.2 Detecting clusters with univariate and bivariate plots of data. 2.2.1 Histograms. 2.2.2 Scatterplots. 2.2.3 Density estimation. 2.2.4 Scatterplot matrices. 2.3 Using lower-dimensional projections of multivariate data for graphical representations. 2.3.1 Principal components analysis of multivariate data. 2.3.2 Exploratory projection pursuit. 2.3.3 Multidimensional scaling. 2.4 Three-dimensional plots and trellis graphics. 2.5 Summary. 3 Measurement of proximity. 3.1 Introduction. 3.2 Similarity measures for categorical data. 3.2.1 Similarity measures for binary data. 3.2.2 Similarity measures for categorical data with more than two levels. 3.3 Dissimilarity and distance measures for continuous data. 3.4 Similarity measures for data containing both continuous and categorical variables. 3.5 Proximity measures for structured data. 3.6 Inter-group proximity measures. 3.6.1 Inter-group proximity derived from the proximity matrix. 3.6.2 Inter-group proximity based on group summaries for continuous data. 3.6.3 Inter-group proximity based on group summaries for categorical data. 3.7 Weighting variables. 3.8 Standardization. 3.9 Choice of proximity measure. 3.10 Summary. 4 Hierarchical clustering. 4.1 Introduction. 4.2 Agglomerative methods. 4.2.1 Illustrative examples of agglomerative methods. 4.2.2 The standard agglomerative methods. 4.2.3 Recurrence formula for agglomerative methods. 4.2.4 Problems of agglomerative hierarchical methods. 4.2.5 Empirical studies of hierarchical agglomerative methods. 4.3 Divisive methods. 4.3.1 Monothetic divisive methods. 4.3.2 Polythetic divisive methods. 4.4 Applying the hierarchical clustering process. 4.4.1 Dendrograms and other tree representations. 4.4.2 Comparing dendrograms and measuring their distortion. 4.4.3 Mathematical properties of hierarchical methods. 4.4.4 Choice of partition – the problem of the number of groups. 4.4.5 Hierarchical algorithms. 4.4.6 Methods for large data sets. 4.5 Applications of hierarchical methods. 4.5.1 Dolphin whistles – agglomerative clustering. 4.5.2 Needs of psychiatric patients – monothetic divisive clustering. 4.5.3 Globalization of cities – polythetic divisive method. 4.5.4 Women’s life histories – divisive clustering of sequence data. 4.5.5 Composition of mammals’ milk – exemplars, dendrogram seriation and choice of partition. 4.6 Summary. 5 Optimization clustering techniques. 5.1 Introduction. 5.2 Clustering criteria derived from the dissimilarity matrix. 5.3 Clustering criteria derived from continuous data. 5.3.1 Minimization of trace(W). 5.3.2 Minimization of det(W). 5.3.3 Maximization of trace (BW1). 5.3.4 Properties of the clustering criteria. 5.3.5 Alternative criteria for clusters of different shapes and sizes. 5.4 Optimization algorithms. 5.4.1 Numerical example. 5.4.2 More on k-means. 5.4.3 Software implementations of optimization clustering. 5.5 Choosing the number of clusters. 5.6 Applications of optimization methods. 5.6.1 Survey of student attitudes towards video games. 5.6.2 Air pollution indicators for US cities. 5.6.3 Aesthetic judgement of painters. 5.6.4 Classification of ‘nonspecific’ back pain. 5.7 Summary. 6 Finite mixture densities as models for cluster analysis. 6.1 Introduction. 6.2 Finite mixture densities. 6.2.1 Maximum likelihood estimation. 6.2.2 Maximum likelihood estimation of mixtures of multivariate normal densities. 6.2.3 Problems with maximum likelihood estimation of finite mixture models using the EM algorithm. 6.3 Other finite mixture densities. 6.3.1 Mixtures of multivariate t-distributions. 6.3.2 Mixtures for categorical data – latent class analysis. 6.3.3 Mixture models for mixed-mode data. 6.4 Bayesian analysis of mixtures. 6.4.1 Choosing a prior distribution. 6.4.2 Label switching. 6.4.3 Markov chain Monte Carlo samplers. 6.5 Inference for mixture models with unknown number of components and model structure. 6.5.1 Log-likelihood ratio test statistics. 6.5.2 Information criteria. 6.5.3 Bayes factors. 6.5.4 Markov chain Monte Carlo methods. 6.6 Dimension reduction – variable selection in finite mixture modelling. 6.7 Finite regression mixtures. 6.8 Software for finite mixture modelling. 6.9 Some examples of the application of finite mixture densities. 6.9.1 Finite mixture densities with univariate Gaussian components. 6.9.2 Finite mixture densities with multivariate Gaussian components. 6.9.3 Applications of latent class analysis. 6.9.4 Application of a mixture model with different component densities. 6.10 Summary. 7 Model-based cluster analysis for structured data. 7.1 Introduction. 7.2 Finite mixture models for structured data. 7.3 Finite mixtures of factor models. 7.4 Finite mixtures of longitudinal models. 7.5 Applications of finite mixture models for structured data. 7.5.1 Application of finite mixture factor analysis to the ‘categorical versus dimensional representation’ debate. 7.5.2 Application of finite mixture confirmatory factor analysis to cluster genes using replicated microarray experiments. 7.5.3 Application of finite mixture exploratory factor analysis to cluster Italian wines. 7.5.4 Application of growth mixture modelling to identify distinct developmental trajectories. 7.5.5 Application of growth mixture modelling to identify trajectories of perinatal depressive symptomatology. 7.6 Summary. 8 Miscellaneous clustering methods. 8.1 Introduction. 8.2 Density search clustering techniques. 8.2.1 Mode analysis. 8.2.2 Nearest-neighbour clustering procedures. 8.3 Density-based spatial clustering of applications with noise. 8.4 Techniques which allow overlapping clusters. 8.4.1 Clumping and related techniques. 8.4.2 Additive clustering. 8.4.3 Application of MAPCLUS to data on social relations in a monastery. 8.4.4 Pyramids. 8.4.5 Application of pyramid clustering to gene sequences of yeasts. 8.5 Simultaneous clustering of objects and variables. 8.5.1 Hierarchical classes. 8.5.2 Application of hierarchical classes to psychiatric symptoms. 8.5.3 The error variance technique. 8.5.4 Application of the error variance technique to appropriateness of behaviour data. 8.6 Clustering with constraints. 8.6.1 Contiguity constraints. 8.6.2 Application of contiguity-constrained clustering. 8.7 Fuzzy clustering. 8.7.1 Methods for fuzzy cluster analysis. 8.7.2 The assessment of fuzzy clustering. 8.7.3 Application of fuzzy cluster analysis to Roman glass composition. 8.8 Clustering and artificial neural networks. 8.8.1 Components of a neural network. 8.8.2 The Kohonen self-organizing map. 8.8.3 Application of neural nets to brainstorming sessions. 8.9 Summary. 9 Some final comments and guidelines. 9.1 Introduction. 9.2 Using clustering techniques in practice. 9.3 Testing for absence of structure. 9.4 Methods for comparing cluster solutions. 9.4.1 Comparing partitions. 9.4.2 Comparing dendrograms. 9.4.3 Comparing proximity matrices. 9.5 Internal cluster quality, influence and robustness. 9.5.1 Internal cluster quality. 9.5.2 Robustness – split-sample validation and consensus trees. 9.5.3 Influence of individual points. 9.6 Displaying cluster solutions graphically. 9.7 Illustrative examples. 9.7.1 Indo-European languages – a consensus tree in linguistics. 9.7.2 Scotch whisky tasting – cophenetic matrices for comparing clusterings. 9.7.3 Chemical compounds in the pharmaceutical industry. 9.7.4 Evaluating clustering algorithms for gene expression data. 9.8 Summary. Bibliography. Index.

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Book SynopsisProvides a comprehensive practical guide to the broadly used statistical language of R assuming no previous knowledge of either statistics or R. A unique focus allows unprecedented coverage of the preparation of data for the application of statistical methods, and the presentation of the results, as well as the statistical applications themselves.Trade Review"The overall presentation is clear and it is very well motivated. ... The text covers a wide range of subjects and is a useful tool for graduate students, practitioners and those interested in statistical computing." (Journal of Applied Statistics, February 2010) "The carefully selected examples (over 300) from different disciplines bring clarity to otherwise difficult, complex statistical concepts. Summing Up: Highly recommended." (CHOICE, July 2009) "This book will most certainly be of great use to upper undergraduate and first-year graduate students or anyone starting to use R for some applied project." (MAA Reviews, April 2009)Table of ContentsPreface. Part I: Data in statistics and R. 1. Basic R. 1.1 Preliminaries. 1.2 Modes. 1.3 Vectors. 1.4 Arithmetic operators and special values. 1.5 Objects. 1.6 Programming. 1.7 Packages. 1.8 Graphics. 1.9 Customizing the workspace. 1.10 Projects. 1.12 Assignments. 2. Data in statistics and in R. 2.1 Types of data. 2.2 Objects that hold data. 2.3 Data organization. 2.4 Data import, export and connections. 2.5 Data manipulation. 2.6 Manipulating strings. 2.7 Assignments. 3. Presenting data. 3.1 Tables and the flavors of apply () 3.2 Bar plots. 3.3 Histograms. 3.4 Dot charts. 3.5 Scatter plots. 3.6 Lattice plots. 3.7 Three-dimensional plots and contours. 3.8 Assignments. Part II: Probability, densities and distributions. 4. Probability and random variables. 4.1 Set theory. 4.2 Trials, events and experiments. 4.3 Definitions and properties of probability. 4.4 Conditional probability and independence. 4.5 Algebra with probabilities. 4.6 Random variables. 4.7 Assignments. 5. Discrete densities and distributions. 5.1 Densities. 5.2 Distribution. 5.3 Properties. 5.4 Expected values. 5.5 Variance and standard deviation. 5.6 The binomial. 5.7 The Poisson. 5.8 Estimating parameters. 5.9 Some useful discrete densities. 5.10 Assignments. 6. Continuous distributions and densities. 6.1 Distributions. 6.2 Densities. 6.3 Properties. 6.4 Expected values. 6.5 Variance and standard deviation. 6.6 Areas under density curves. 6.7 Inverse distributions and simulations. 6.8 Some useful continuous densities. 6.9 Assignments. 7. The normal and sampling densities. 7.1 The normal density. 7.2 Applications of the normal. 7.3 Data transformations. 7.4 Random samples and sampling densities. 7.5 A detour: using R efficiently. 7.6 The sampling density of the mean. 7.7 The sampling density of proportion. 7.8 The sampling density of intensity. 7.9 The sampling density of variance. 7.10 Bootstrap: arbitrary parameters of arbitrary densities. 7.11 Assignments. Part III: Statistics. 8. Exploratory data analysis. 8.1 Graphical methods. 8.2 Numerical summaries. 8.3 Visual summaries. 8.4 Assignments. 9. Point and interval estimation. 9.1 Point estimation. 9.2 Interval estimation. 9.3 Point and interval estimation for arbitrary densities. 9.4 Assignments. 10. Single sample hypotheses testing. 10.1 Null and alternative hypotheses. 10.2 Large sample hypothesis testing. 10.3 Small sample hypotheses testing. 10.4 Arbitrary parameters of arbitrary densities. 10.5 p-values. 10.6 Assignments. 11. Power and sample size for single samples. 11.1 Large sample. 11.2 Small samples. 11.3 Power and sample size for arbitrary densities. 11.4 Assignments. 12. Two samples. 12.1 Large samples. 12.2 Small samples. 12.3 Unknown densities. 12.4 Assignments. 13. Power and sample size for two samples. 13.1 Two means from normal populations. 13.2 Two proportions. 13.3 Two rates. 13.4 Assignments. 14. Simple linear regression. 14.1 Simple linear models. 14.2 Estimating regression coefficients. 14.3 The model goodness of fit. 14.4 Hypothesis testing and confidence intervals. 14.5 Model assumptions. 14.6 Model diagnostics. 14.7 Power and sample size for the correlation coefficient. 14.8 Assignments. 15. Analysis of variance. 15.1 One-way, fixed-effects ANOVA. 15.2 Non-parametric one-way ANOVA. 15.3 One-way, random-effects ANOVA. 15.4 Two-way ANOVA. 15.5 Two-way linear mixed effects models. 15.6 Assignments. 16. Simple logistic regression. 16.1 Simple binomial logistic regression. 16.2 Fitting and selecting models. 16.3 Assessing goodness of fit. 16.4 Diagnostics. 16.5 Assignments. 17. Application: the shape of wars to come. 17.1 A statistical profile of the war in Iraq. 17.2 A statistical profile of the second Intifada. References. R Index. General Index.

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Book SynopsisDetailing the link between computational models and physical models, Modeling and Simulation in the Medical and Health Sciences encourages a more uniform discussion of simulation within both the engineering and medical domains.Table of ContentsContributors. Foreword. Preface. Part One Fundamentals of Medical and Health Sciences Modeling and Simulation. 1 Introduction to Modeling and Simulation in the Medical and Health Sciences (Catherine M. Banks). 2 The Practice of Modeling and Simulation: Tools of the Trade (John A. Sokolowski). Part Two. Modeling for the Medical and Health Sciences. 3 Mathematical Models of Tumor Growth and Wound Healing (John A. Adam). 4 Physical Modeling (Stacie I. Ringleb). Part Three. Modeling and Simulation Applications. 5 Humans as Models (C. Donald Combs). 6 Modeling the Human System (Mohammed Ferdjallah and Gyu Tae Kim). 7 Robotics (Richard Lee). 8 Training (Paul E. Phrampus). 9 Patient Care (Eugene Santos Jr, Joseph Rosen, Keum Joo Kim, Fei Yu, Dequing Li, Elizabeth Jacob, Lindsay Katona). 10 Future of Modeling and Simulation in the Medical and Health Sciences (Richard M. Satava). Appendix. Index.

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Book SynopsisInsightful observations on common question evaluation methods and best practices for data collection in survey research Featuring contributions from leading researchers and academicians in the field of survey research, Question Evaluation Methods: Contributing to the Science of Data Quality sheds light on question response error and introduces an interdisciplinary, cross-method approach that is essential for advancing knowledge about data quality and ensuring the credibility of conclusions drawn from surveys and censuses. Offering a variety of expert analyses of question evaluation methods, the book provides recommendations and best practices for researchers working with data in the health and social sciences. Based on a workshop held at the National Center for Health Statistics (NCHS), this book presents and compares various question evaluation methods that are used in modern-day data collection and analysis. Each section includes an introductionTable of ContentsContributors ix Preface xi 1 Introduction 1 Jennifer Madans, Kristen Miller, Aaron Maitland, and Gordon Willis I BEHAVIOR CODING 5 2 Coding the Behavior of Interviewers and Respondents to Evaluate Survey Questions 7 Floyd J. Fowler, Jr. 3 Response 1 to Fowler’s Chapter: Coding the Behavior of Interviewers and Respondents to Evaluate Survey Questions 23 Nora Cate Schaeffer and Jennifer Dykema 4 Response 2 to Fowler’s Chapter: Coding the Behavior of Interviewers and Respondents to Evaluate Survey Questions 41 Alisú Schoua-Glusberg II COGNITIVE INTERVIEWING 49 5 Cognitive Interviewing 51 Kristen Miller 6 Response 1 to Miller’s Chapter: Cognitive Interviewing 77 Gordon Willis 7 Response 2 to Miller’s Chapter: Cognitive Interviewing 93 Frederick G. Conrad III ITEM RESPONSE THEORY 103 8 Applying Item Response Theory for Questionnaire Evaluation 105 Bryce B. Reeve 9 Response 1 to Reeve’s Chapter: Applying Item Response Theory for Questionnaire Evaluation 125 Ron D. Hays 10 Response 2 to Reeve’s Chapter: Applying Item Response Theory for Questionnaire Evaluation 137 Clyde Tucker, Brian Meekins, Jennifer Edgar, and Paul P. Biemer IV LATENT CLASS ANALYSIS 151 11 Some Issues in the Application of Latent Class Models for Questionnaire Design 153 Paul P. Biemer and Marcus Berzofsky 12 Response 1 to Biemer and Berzofsky’s Chapter: Some Issues in the Application of Latent Class Models for Questionnaire Design 187 Frauke Kreuter 13 Response 2 to Biemer and Berzofsky’s Chapter: Some Issues in the Application of Latent Class Models for Questionnaire Design 199 Janet A. Harkness and Timothy P. Johnson V SPLIT-SAMPLE EXPERIMENTS 213 14 Experiments for Evaluating Survey Questions 215 Jon A. Krosnick 15 Response 1 to Krosnick’s Chapter: Experiments for Evaluating Survey Questions 239 Johnny Blair 16 Response 2 to Krosnick’s Chapter: Experiments for Evaluating Survey Questions 253 Theresa DeMaio and Stephanie Willson VI MULTITRAIT-MULTIMETHOD EXPERIMENTS 263 17 Evaluating the Reliability and Validity of Survey Interview Data Using the MTMM Approach 265 Duane F. Alwin 18 Response to Alwin’s Chapter: Evaluating the Reliability and Validity of Survey Interview Data Using the MTMM Approach 295 Peter Ph. Mohler VII FIELD-BASED DATA METHODS 319 19 Using Field Tests to Evaluate Federal Statistical Survey Questionnaires 321 Brian A. Harris-Kojetin and James M. Dahlhamer Index 345

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Book Synopsis* Presents a much-needed practical guide to statistical spatial analysis on a network, in a logical, user-friendly order. * Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics.Trade Review“Students and researchers studying spatial statistics, spatial analysis, geography, GIS, OR, traffic accident analysis, criminology, retail marketing, facility management and ecology will benefit from this book.” (Zentralblatt MATH, 1 May 2013) Table of ContentsPreface Acknowledgements Chapter 1 Introduction 1.1 What is network spatial analysis? 1.1.1 Network events: events on and alongside networks 1.1.2 Planar spatial analysis and its limitations 1.1.3 Network spatial analysis and its salient features 1.2 Review of studies of network events 1.2.1 Snow’s study on cholera around Broad Street 1.2.2 Traffic accidents 1.2.3 Road-kills 1.2.4 Street crimes 1.2.5 Events on river networks and coastlines 1.2.6 Other events on networks 1.2.7 Events alongside networks 1.3 Outline of the book 1.3.1 Structure of chapters 1.3.2 Questions solved by network spatial methods 1.3.3 How to study this book Chapter 2 Modeling events on and alongside networks 2.1 Modeling the real world 2.1.1 Object-based model 　　 2.1.1.1 Spatial attributes 2.1.1.2 Nonspatial attributes 2.1.2 Field-based model 2.1.3 Vector data model 2.1.4 Raster data model 2.2 Modeling networks 2.2.1 Object-based model for networks 2.2.1.1 Geometric networks 2.2.1.2 Graph for a geometric network 2.2.2 Field-based model for networks 2.2.3 Data models for networks 2.3 Modeling entities on and alongside networks 2.3.1 Objects on network space 2.3.2 Field functions on network space 2.4 Stochastic processes on network space 2.4.1 Object-based model for stochastic spatial events on network space 2.4.2 Binomial point processes on network space 2.4.3 Edge effects 2.4.4 Uniform network transformation Chapter 3 Basic computational methods for network spatial analysis 3.1 Data structures for one-layer networks 3.1.1 Planar networks 3.1.2 Winged-edge data structures 3.1.3 Efficient access and enumeration of local information 3.1.4 Attribute data representation 3.1.5 Local modifications of a network 3.1.5.1 Inserting new nodes 3.1.5.2 New nodes resulting from overlying two networks 3.1.5.3 Deleting existing nodes 3.2 Data Structures for nonplanar networks 3.2.1 Multiple-layer networks 3.2.2 General nonplanar networks 3.3 Basic Geometric Computations 3.3.1 Computational methods for line segments 3.3.1.1 Right-turn test 3.3.1.2 Intersection test for two line segments 3.3.1.3 Enumeration of line segment intersections 3.3.2 Time complexity as a measure of efficiency 3.3.3 Computational methods for polygons 3.3.3.1 Area of a polygon 3.3.3.2 Center of gravity of a polygon 3.3.3.3 Inclusion test of a point with respect to a polygon 3.3.3.4 Polygon-line intersection 3.3.3.5 Polygon intersection test 3.3.3.6 Extraction of a subnetwork inside a polygon 3.3.3.7 Set-theoretic computations 3.3.3.8 Nearest point on the edges of a polygon from a point in the polygon 3.3.3.9 Frontage interval 3.4. Basic computational methods on networks 3.4.1 Single-source shortest paths 3.4.1.1 Network connectivity test 3.4.1.2 Shortest-path tree 3.4.1.3 Extended shortest-path tree 3.4.1.4 All nodes within a prespecified distance 3.4.1.5 Center of a network 3.4.1.6 Heap data structure 3.4.2 Shortest path between two nodes 3.4.3 Minimum spanning tree on a network 3.4.4 Monte Carlo simulation for generating random points on a network Chapter 4 Network Voronoi diagrams 4.1 Ordinary network Voronoi diagram 4.1.1 Planar versus network Voronoi diagrams 4.1.2 Geometric properties of the ordinary network Voronoi diagram 4.2 Generalized network Voronoi diagrams 4.2.1 Directed network Voronoi diagram 4.2.2 Weighted network Voronoi diagram 4.2.3 k-th nearest point network Voronoi diagram 4.2.4 Line and polygon network Voronoi diagram 4.2.5 Point-set network Voronoi diagram 4.3 Computational methods for network Voronoi diagrams 4.3.1 Multi-start Dijkstra method 4.3.2 Computational method for the ordinary network Voronoi diagram 4.3.3 Computational method for the directed network Voronoi diagram 4.3.4 Computational method for the weighted network Voronoi diagram 4.3.5 Computational method for the -th nearest point network Voronoi diagram 4.3.6 Computational method for the line and polygon network Voronoi diagrams 4.3.7 Computational method for the point-set network Voronoi diagram Chapter 5 Network nearest-neighbor distance methods 5.1 Network auto nearest-neighbor distance method 5.1.1 Network local auto nearest-neighbor distance method 5.1.2 Network global auto nearest-neighbor distance method 5.2 Network cross nearest-neighbor distance method 5.2.1 Network local cross nearest-neighbor distance method 5.2.2 Network global cross nearest-neighbor distance method 5.3 Network nearest-neighbor distance method for lines 5.4 Computational methods for network nearest-neighbor distance methods 5.4.1 Computational methods for network auto nearest-neighbor distance methods 5.4.1.1 Computational methods for network local auto nearest-neighbor distance method 5.4.1.2 Computational methods for network global auto nearest-neighbor distance method 5.4.2 Computational methods for network cross nearest-neighbor distance methods 5.4.2.1 Computational methods for network local cross nearest-neighbor distance method 5.4.2.2 Computational methods for network global cross nearest-neighbor distance method Chapter 6 Network K function methods 6.1 Network auto K function methods 6.1.1 Network local auto K function method 6.1.2 Network global auto K function method 6.2 Network cross K function methods 6.2.1 Network local cross K function method 6.2.2 Network global cross K function method 6.2.3 Network global Voronoi cross K function method 6.3 Network K function methods in relation to geometric characteristics of a network 6.3.1 Relationship between the shortest-path distance and the Euclidean distance 6.3.2 Network global auto K function in relation to the level-of-detail of a network 6.4 Computational methods for the network K function methods 6.4.1 Computational methods for the network auto K function methods 6.4.1.1 Computational methods for the network local auto K function method 6.4.1.2 Computational methods for the network global auto K function method 6.4.2 Computational methods for the network cross K function methods 6.4.2.1 Computational methods for the network local auto K function method 6.4.2.3 Computational methods for the network global cross K function method 6.4.2.3 Computational methods for the network global Voronoi cross K function method Chapter 7 Network spatial autocorrelation 7.1 Classification of spatial autocorrelations 7.2 Spatial randomness of the attribute values of network cells 7.2.1 Permutation spatial randomness 7.2.2 Normal variate spatial randomness 7.3 Network Moran’s I statistics 7.3.1 Network local Moran’s I statistic 7.3.2 Network global Moran’s I statistic 7.4 Computational methods for network Moran’s I statistics Chapter 8 Network point cluster analysis and clumping method 8.1 Network point cluster analysis 8.1.1 General hierarchical point cluster analysis 8.1.2 Hierarchical point clustering methods with specific intercluster distances 8.1.2.1 Network closest-pair point clustering method 8.1.2.2Network farthest-pair point clustering method 8.1.2.3 Network average-pair point clustering method 8.1.2.4 Network point clustering methods with other interclaster distances 8.2 Network clumping method 8.2.1 Relation to network point cluster analysis 8.2.2 Statistical test with respect to the number of clumps 8.3 Computational methods for network point cluster analysis and clumping method 8.3.1 General computational framework 8.3.2 Computational methods for individual intercluster distances 8.3.2.1 Computational methods for the network closest-pair point clustering method 8.3.2.1 Computational methods for the network farthest-pair point clustering method 8.3.2.3 Computational methods for the network average-pair point clustering method 8.3.3 Computational aspects of the network clumping method Chapter 9 Network point density estimation methods 9.1 Network histograms 9.1.1 Network cell histograms 9.1.2 Network Voronoi cell histograms 9.1.3 Network cell-count method 9.2 Network kernel density estimation methods 9.2.1 Network kernel functions 9.2.2 Equal-split discontinuous kernel functions 9.2.3 Equal-split continuous kernel functions 9.3 Computational methods for network point density estimation 9.3.1 Computational methods for network cell histograms with equal-length network cells 9.3.2 Computational method for equal-split discontinuous kernel density functions 9.3.3 Computational method for equal-split continuous kernel density functions Chapter 10 Network spatial interpolation 10.1 Network inverse-distance weighting 10.1.1 Concepts of neighborhoods on a network 10.1.2 Network inverse-distance weighting predictor 10.2 Network kriging 10.2.1 Network kriging models 10.2.2 Concepts of stationary processes on a network 10.2.3 Network variogram models 10.2.4 Network kriging predictors 10.3 Computational methods for network spatial interpolation 10.3.1 Computational methods for network inverse-distance weighing 10.3.2 Computational methods for network kriging Chapter 11 Network Huff model 11.1 Concepts of the network Huff model 11.1.1 Huff models 11.1.2 Dominant market subnetworks 11.1.3 Huff-based demand estimation 11.1.4 Huff-based locational optimization 11.2 Computational methods for the Huff-based demand estimation 11.2.1 Shortest-path tree distance 11.2.2 Choice probabilities in terms of shortest-path tree distances 11.2.3 Analytical formula for the Huff-based demand estimation 11.2.4 Computational tasks and their time complexities for the Huff-based demand estimation 11.3 Computational methods for the Huff-based locational optimization 11.3.1 Demand function for a newly entering store 11.3.2 Topologically invariant shortest-path trees 11.3.3 Topologically invariant link sets 11.3.4 Numerical method for the Huff-based locational optimization 11.3.5 Computational tasks and their time complexities for the Huff-based locational optimization Chapter 12 GIS-based tools for spatial analysis along networks and their application 12.1 Preprocessing tools in SANET 12.1.1 Tool for testing network connectedness 12.1.2 Tool for assigning points to the nearest points on a network 12.1.3 Tool for computing shortest-path distances between points 12.1.4 Tool for generating random points on a network 12.2 Statistical tools in SANET and their applications 12.2.1 Tools for network Voronoi diagrams and their application 12.2.2 Tools for network nearest neighbor distance methods and their application 12.2.2.1 Network global auto nearest-neighbor distance method 12.2.2.2 Network global cross nearest-neighbor distance method 12.2.3 Tools for network K function methods and their application 12.2.3.1 Network global auto K function method 12.2.3.2 Network global cross K function method 12.2.3.3 Network global Voronoi cross K function method 12.2.3.4 Network local cross K function method 12.2.4 Tools for network cluster analysis and their application 12.2.5 Tools for network kernel density estimation methods and their application 12.2.6 Tools for network spatial interpolation methods and their application References Index

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Book SynopsisStatistical complex survey analysis is a means to analyse the results, and gain information about a large population based on a complex survey of a sample of that population. A complex survey is a sample survey that divides the population into subgroups and collecting information from clusters within each subgroup and combining the results.Trade Review"As in the previous edition, this book is a good resource for practitioners and cross-disciplinary researchers who use data from complex survey designs." (Journal of the American Statistical Association, March 2006) "The first edition of the book was one of the first books in the excellent Wiley U.K. series on Statistics in Practice." (Technometrics, May 2005)Table of ContentsPreface. 1. Introduction. 2. Basic Sampling Techniques. 2.1 Basic definitions. 2.2 The Province’91 population. 2.3 Simple random sampling and design effect. 2.4 Systematic sampling and intra-class correlation. 2.5 Selection with probability proportional to size. 3. Further Use of Auxiliary Information. 3.1 Stratified sampling. 3.2 Cluster sampling. 3.3 Model-assisted estimation. 3.4 Efficiency comparison using design effects. 4. Handling Nonsampling Errors. 4.1 Reweighting. 4.2 Imputation. 4.3 Chapter summary and further reading. 5. Linearization and Sample Reuse in Variance Estimation. 5.1 The Mini-Finland Health Survey. 5.2 Ratio estimators. 5.3 Linearization method. 5.4 Sample reuse methods. 5.5 Comparison of variance estimators. 5.6 The Occupational Health C are Survey. 5.7 Linearization method for covariance-matrix estimation. 5.8 Chapter summary and further reading. 6. Model-assisted Estimation for Domains. 6.1 Framework for domain estimation. 6.2 Estimator type and model choice. 6.3 Construction of estimators and model specification. 6.4 Further comparison of estimators. 6.5 Chapter summary and further reading. 7. Analysis of One-way and Two-way Tables. 7.1 Introductory example. 7.2 Simple goodness-of-fit test. 7.3 Preliminaries for tests for two-way tables. 7.4 Test of homogeneity. 7.5 Test of independence. 7.6 Chapter summary and further reading. 8. Multivariate Survey Analysis. 8.1 Range of methods. 8.2 Types of models and options for analysis. 8.3 Analysis of categorical data. 8.4 Logistic and linear regression. 8.5 Chapter summary and further reading. 9. More Detailed Case Studies. 9.1 Monitoring quality in a long-term transport survey. 9.2 Estimation of mean salary in a business survey. 9.3 Model selection in a socioeconomic survey. 9.4 Multi-level modelling in an educational survey. References. Author Index. Subject Index. Web Extension. In addition to the printed book, electronic materials supporting the use of the book can be found in the web extension.

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Book SynopsisThis book takes the novel approach to cover both the sequence and structure analysis of proteins in one volume and from an algorithmic perspective. Key features of the book include:* Provides a comprehensive introduction to the analysis of protein sequence and structure analysis.Table of ContentsPreface. Acknowledgements. Part I: SEQUENCE ANALYSIS. 1. Pairwise Global Alignment of Sequences. 1.1 Alignment and Evolution. 1.2 What is an Alignment? 1.3 A Scoring Scheme for the Model. 1.4 Finding Highest-Scoring Alignments with Dynamic Programming. 1.4.1 Determine Hi,j. 1.4.2 Use of matrices. 1.4.3 Finding the alignments that give the highest score. 1.4.4 Gaps. 1.5 Scoring Matrices. 1.6 Scoring Gaps: Gap Penalties. 1.7 Dynamic Programming for General Gap Penalty. 1.8 Dynamic Programming for Affine Gap Penalty. 1.9 Alignment Score and Sequence Distance. 1.10 Exercises. 1.11 Bibliographic notes. 2 Pairwise Local Alignment and Database Search. 2.1 The Basic Operation: Comparing Two Sequences. 2.2 Dot Matrices. 2.2.1 Filtering. 2.2.2 Repeating segments. 2.3 Dynamic Programming. 2.3.1 Initialization. 2.3.2 Finding the best local alignments. 2.3.3 Algorithms. 2.3.4 Scoring matrices and gap penalties. 2.4 Database Search: BLAST. 2.4.1 The procedure. 2.4.2 Preprocess the query: make the word list. 2.4.3 Scanning the database sequences. 2.4.4 Extending to HSP. 2.4.5 Introducing gaps. 2.4.6 Algorithm. 2.5 Exercises. 2.6 Bibliographic notes. 3. Statistical Analysis. 3.1 Hypothesis Testing for Sequence Homology. 3.1.1 Random generation of sequences. 3.1.2 Use of Z values for estimating the statistical significance. 3.2 Statistical Distributions. 3.2.1 Poisson probability distribution. 3.2.2 Extreme value distributions. 3.3 Theoretical Analysis of Statistical Significance. 3.3.1 The P value has an extreme value distribution. 3.3.2 Theoretical analysis for database search. 3.4 Probability Distributions for Gapped Alignments. 3.5 Assessing and Comparing Programs for Database Search. 3.5.1 Sensitivity and specificity. 3.5.2 Discrimination power. 3.5.3 Using more sequences as queries. 3.6 Exercises. 3.7 Bibliographic notes. 4 .Multiple Global Alignment and Phylogenetic Trees. 4.1 Dynamic Programming. 4.1.1 SP score of multiple alignments. 4.1.2 A pruning algorithm for the DP solution. 4.2 Multiple Alignments and Phylogenetic Trees. 4.3 Phylogeny. 4.3.1 The number of different tree topologies. 4.3.2 Molecular clock theory. 4.3.3 Additive and ultrametric trees. 4.3.4 Different approaches for reconstructing phylogenetic trees. 4.3.5 Distance-based construction. 4.3.6 Rooting of trees. 4.3.7 Statistical test: bootstrapping. 4.4 Progressive Alignment. 4.4.1 Aligning two subset alignments. 4.4.2 Clustering. 4.4.3 Sequence weights. 4.4.4 CLUSTAL. 4.5 Other Approaches. 4.6 Exercises. 4.7 Bibliographic notes. 5. Scoring Matrices. 5.1 Scoring Matrices Based on Physio-Chemical Properties. 5.2 PAM Scoring Matrices. 5.2.1 The evolutionary model. 5.2.2 Calculate substitution matrix. 5.2.3 Matrices for general evolutionary time. 5.2.4 Measuring sequence similarity by use of Mτ. 5.2.5 Odds matrices. 5.2.6 Scoring matrices (log-odds matrices). 5.2.7 Estimating the evolutionary distance. 5.3 BLOSUM Scoring Matrices. 5.3.1 Log-odds matrix. 5.3.2 Developing scoring matrices for different evolutionary distances. 5.4 Comparing BLOSUM and PAM Matrices. 5.5 Optimal Scoring Matrices. 5.5.1 Analysis for one sequence. 5.6 Exercises. 5.7 Bibliographic notes. 6. Profiles. 6.1 Constructing a Profile. 6.1.1 Notation. 6.1.2 Removing rows and columns. 6.1.3 Position weights. 6.1.4 Sequence weights. 6.1.5 Treating gaps. 6.2 Searching Databases with Profiles. 6.3 Iterated BLAST: PSI-BLAST. 6.3.1 Making the multiple alignment. 6.3.2 Constructing the profile. 6.4 HMM Profile. 6.4.1 Definitions for an HMM. 6.4.2 Constructing a profile HMM for a protein family. 6.4.3 Comparing a sequence with an HMM. 6.4.4 Protein family databases. 6.5 Exercises. 6.6 Bibliographic notes. 7. Sequence Patterns. 7.1 The PROSITE Language. 7.2 Exact/Approximate Matching. 7.3 Defining Pattern Classes by Imposing Constraints. 7.4 Pattern Scoring: Information Theory. 7.4.1 Information theory. 7.4.2 Scoring patterns. 7.5 Generalization and Specialization. 7.6 Pattern Discovery: Introduction. 7.7 Comparison-Based Methods. 7.7.1 Pivot-based methods. 7.7.2 Tree progressive methods. 7.8 Pattern-Driven Methods: Pratt. 7.8.1 The main procedure. 7.8.2 Preprocessing. 7.8.3 The pattern space. 7.8.4 Searching. 7.8.5 Ambiguous components. 7.8.6 Specialization. 7.8.7 Pattern scoring. 7.9 Exercises. 7.10 Bibliographic notes. Part II: STRUCTURE ANALYSIS 8. Structures and Structure Descriptions. 8.1 Units of Structure Descriptions. 8.2 Coordinates. 8.3 Distance Matrices. 8.4 Torsion Angles. 8.5 Coarse Level Description. 8.5.1 Line segments (sticks). 8.5.2 Ellipsoid. 8.5.3 Helices. 8.5.4 Strands and sheets. 8.5.5 Topology of Protein Structure (TOPS). 8.6 Identifying the SSEs. 8.6.1 Use of distance matrices. 8.6.2 Define Secondary Structure of Proteins (DSSP). 8.7 Structure Comparison. 8.7.1 Structure descriptions for comparison. 8.7.2 Structure representation. 8.8 Framework for Pairwise Structure Comparison. 8.9 Exercises. 8.10 Bibliographic notes. 9. Superposition and Dynamic Programming. 9.1 Superposition. 9.1.1 Coordinate RMSD. 9.1.2 Distance RMSD. 9.1.3 Using RMSD as scoring of structure similarities. 9.2 Alternating Superposition and Alignment. 9.3 Double Dynamic Programming. 9.3.1 Low-level scoring matrices. 9.3.2 High-level scoring matrix. 9.3.3 Iterated double dynamic programming. 9.4 Similarity of the Methods. 9.5 Exercises. 9.6 Bibliographic notes. 10. Geometric Techniques. 10.1 Geometric Hashing. 10.1.1 Two-dimensional geometric hashing. 10.1.2 Geometric hashing for structure comparison. 10.1.3 Geometric hashing for SSE representation. 10.1.4 Clustering. 10.2 Distance Matrices. 10.2.1 Measuring the similarity of distance (sub)matrices. 10.3 Exercises. 10.4 Bibliographic notes. 11. Clustering: Combining Local Similarities. 11.1 Compatibility and Consistency. 11.2 Searching for Seed Matches. 11.3 Consistency. 11.3.1 Test for consistency. 11.3.2 Overlapping clusters. 11.4 Clustering Algorithms. 11.4.1 Linear clustering. 11.4.2 Hierarchical clustering. 11.5 Clustering by Use of Transformations. 11.5.1 Comparing transformations. 11.5.2 Calculating the new transformation. 11.5.3 Algorithm. 11.6 Clustering by Use of Relations. 11.6.1 How many relations to compare? 11.6.2 Geometric relation. 11.6.3 Distance relation. 11.6.4 Use of graph theory. 11.7 Refinement. 11.8 Exercises. 11.9 Bibliographic notes. 12. Significance and Assessment of Structure Comparisons. 12.1 Constructing Random Structure Models. 12.1.1 Use of distance geometry. 12.2 Use of Structure Databases. 12.2.1 Constructing nonredundant subsets. 12.2.2 Demarcation line for similarity. 12.3 Reversing the Protein Chain. 12.4 Randomized Alignment Models. 12.5 Assessing Comparison and Scoring Methods. 12.6 Is RMSD Suitable for Scoring? 12.7 Scoring and Biological Significance. 12.8 Exercises. 12.9 Bibliographic notes. 13. Multiple Structure Comparison. 13.1 Multiple Superposition. 13.2 Progressive Structure Alignment. 13.2.1 Scoring. 13.2.2 Construction of consensus. 13.3 Finding a Common Core from a Multiple Alignment. 13.4 Discovering Common Cores. 13.4.1 Finding the multiple seed matches. 13.4.2 Pairwise clustering. 13.4.3 Determining common cores. 13.4.4 Scoring clusters. 13.5 Local Structure Patterns. 13.5.1 Local packing patterns. 13.5.2 Discovering packing patterns. 13.5.3 The approach. 13.5.4 Scoring the packing motifs. 13.6 Exercises. 13.7 Bibliographic notes. 14. Protein Structure Classification. 14.1 Protein Domains. 14.2 An Ising Model for Domain Identification. 14.3 Domain Classes. 14.3.1 Mainly-? domains. 14.3.2 Mainly-? domains. 14.3.3 ?–? domains. 14.4 Folds. 14.5 Automatic Approaches to Classification. 14.6 Databases for Structure Classification. 14.7 FSSP-Dali Domain Dictionary. 14.8 CATH. 14.8.1 Domains. 14.8.2 Class. 14.8.3 Architecture. 14.8.4 Topology (fold family). 14.8.5 Homologous superfamily. 14.8.6 Sequence families. 14.8.7 The CATH classification procedure. 14.9 Classification Based on Sticks. 14.10 Exercises. 14.11 Bibliographic notes. Part III: SEQUENCE-STRUCTUREANALYSIS. 15. Structure Prediction: Threading. 15.1 Protein Secondary Structure Prediction. 15.1.1 Artificial neural networks. 15.1.2 The PHD program. 15.1.3 Accuracy in secondary structure prediction. 15.2 Threading. 15.3 Methods Based on Sequence Alignment. 15.3.1 The 3D–1D matching method. 15.3.2 The FUGUE method. 15.4 Methods Using 3D Interactions. 15.4.1 Potentials of mean force. 15.4.2 Towards modelling methods. 15.5 Alignment Methods. 15.5.1 Frozen approximation. 15.5.2 Double Dynamic Programming. 15.6 Multiple Sequence/Structure Threading. 15.6.1 Simple multiple sequence threading. 15.7 Combined Sequence/Threading Methods. 15.8 Assessment of Threading Methods. 15.8.1 Fold recognition. 15.8.2 Alignment accuracy. 15.8.3 CASP and CAFASP. 15.9 Bibliographic notes. Appendix A: Basics in Mathematics, Probability and Algorithms. A.1 Mathematical Formulae and Notation. A.2 Boolean Algebra. A.3 Set Theory. A.4 Probability. A.4.1 Permutation and combination. A.4.2 Probability distributions. A.4.3 Expected value. A.5 Tables, Vectors and Matrices. A.6 Algorithmic Language. A.6.1 Alternatives. A.6.2 Loops. A.7 Complexity. Appendix B: Introduction to Molecular Biology. B.1 The Cell and the Molecules of Life: DNA–RNA Proteins. B.2 Chromosomes and Genes. B.3 The Central Dogma of Molecular Biology. B.4 The Genetic Code. B.5 Protein Function. B.5.1 The gene ontology. B.6 Protein Structure. B.7 Evolution. B.8 Insulin Example. B.9 Bibliographic notes. References. Index.

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Book SynopsisDespite its haphazard growth, the Web hides powerful underlying regularities -- from the organization of its links to the patterns found in its use by millions of users. Probabilistic modelling allows many of these regularities to be predicted on the basis of theoretical models based on statistical methodology.Trade Review"…I congratulate the authors on a very well-researched and well-written publication." (Technometrics, August 2004, Vol. 46, No. 3) “…fascinating …I highly recommend this book…” (Short Book Reviews, August 2004) “…a very well-researched and well-written publication.” (Technometrics, August 2004) Table of ContentsPreface. 1 Mathematical Background. 1.1 Probability and Learning from a Bayesian Perspective. 1.2 Parameter Estimation from Data. 1.3 Mixture Models and the Expectation Maximization Algorithm. 1.4 Graphical Models. 1.5 Classification. 1.6 Clustering. 1.7 Power-Law Distributions. 1.8 Exercises. 2 Basic WWW Technologies. 2.1 Web Documents. 2.2 Resource Identifiers: URI, URL, and URN. 2.3 Protocols. 2.4 Log Files. 2.5 Search Engines. 2.6 Exercises. 3 Web Graphs. 3.1 Internet and Web Graphs. 3.2 Generative Models for the Web Graph and Other Networks. 3.3 Applications. 3.4 Notes and Additional Technical References. 3.5 Exercises. 4 Text Analysis. 4.1 Indexing. 4.2 Lexical Processing. 4.3 Content-Based Ranking. 4.4 Probabilistic Retrieval. 4.5 Latent Semantic Analysis. 4.6 Text Categorization. 4.7 Exploiting Hyperlinks. 4.8 Document Clustering. 4.9 Information Extraction. 4.10 Exercises. 5 Link Analysis. 5.1 Early Approaches to Link Analysis. 5.2 Nonnegative Matrices and Dominant Eigenvectors. 5.3 Hubs and Authorities: HITS. 5.4 PageRank. 5.5 Stability. 5.6 Probabilistic Link Analysis. 5.7 Limitations of Link Analysis. 6 Advanced Crawling Techniques. 6.1 Selective Crawling. 6.2 Focused Crawling. 6.3 Distributed Crawling. 6.4 Web Dynamics. 7 Modeling and Understanding Human Behavior on the Web. 7.1 Introduction. 7.2 Web Data and Measurement Issues. 7.3 Empirical Client-Side Studies of Browsing Behavior. 7.4 Probabilistic Models of Browsing Behavior. 7.5 Modeling and Understanding Search Engine Querying. 7.6 Exercises. 8 Commerce on the Web: Models and Applications. 8.1 Introduction. 8.2 Customer Data on theWeb. 8.3 Automated Recommender Systems. 8.4 Networks and Recommendations. 8.5 Web Path Analysis for Purchase Prediction. 8.6 Exercises. Appendix A: Mathematical Complements. A.1 Graph Theory. A.2 Distributions. A.3 Singular Value Decomposition. A.4 Markov Chains. A.5 Information Theory. Appendix B: List of Main Symbols and Abbreviations. References. Index.

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Book SynopsisFinancial risk management has become a popular practice amongst financial institutions to protect against the adverse effects of uncertainty caused by fluctuations in interest rates, exchange rates, commodity prices, and equity prices. New financial instruments and mathematical techniques are continuously developed and introduced in financial practice. These techniques are being used by an increasing number of firms, traders and financial risk managers across various industries. Risk and Financial Management: Mathematical and Computational Methods confronts the many issues and controversies, and explains the fundamental concepts that underpin financial risk management. Provides a comprehensive introduction to the core topics of risk and financial management. Adopts a pragmatic approach, focused on computational, rather than just theoretical, methods. Bridges the gap between theory and practice in financial risk management Includes coverage ofTrade Review"The strength of the book is its view of practical aspects and the focus on embedding mathematical finance in the daily work of traders." (Mathematical Reviews, 2005k) “…has much to recommend it for the practitioner in risk or finance.” (Journal of the Royal Statistical Society, Series A, Vol.168, No.2, March 2005) "...All in all, this book gives a refreshing approach..." (Short Book Review, Vol.24, No.3 December 2004) "..this book will serve to give mathematicians an insight into financial decision making" (Zentralblatt MATH, 11th March 2007 ) Table of ContentsPreface. Part I: Finance and Risk Management. Chapter 1: Potpourri. 1.1 Introduction. 1.2 Theoretical finance and decision making. 1.3 Insurance and actuarial science. 1.4 Uncertainty and risk in finance. 1.4.1 Foreign exchange risk. 1.4.2 Currency risk. 1.4.3 Credit risk. 1.4.4 Other risks. 1.5 Financial physics. Selected introductory reading. Chapter 2: Making Economic Decisions under Uncertainty. 2.1 Decision makers and rationality. 2.1.1 The principles of rationality and bounded rationality. 2.2 Bayes decision making. 2.2.1 Risk management. 2.3 Decision criteria. 2.3.1 The expected value (or Bayes) criterion. 2.3.2 Principle of (Laplace) insufficient reason. 2.3.3 The minimax (maximin) criterion. 2.3.4 The maximax (minimin) criterion. 2.3.5 The minimax regret or Savage’s regret criterion. 2.4 Decision tables and scenario analysis. 2.4.1 The opportunity loss table. 2.5 EMV, EOL, EPPI, EVPI. 2.5.1 The deterministic analysis. 2.5.2 The probabilistic analysis. Selected references and readings. Chapter 3: Expected Utility. 3.1 The concept of utility. 3.1.1 Lotteries and utility functions. 3.2 Utility and risk behaviour. 3.2.1 Risk aversion. 3.2.2 Expected utility bounds. 3.2.3 Some utility functions. 3.2.4 Risk sharing. 3.3 Insurance, risk management and expected utility. 3.3.1 Insurance and premium payments. 3.4 Critiques of expected utility theory. 3.4.1 Bernoulli, Buffon, Cramer and Feller. 3.4.2 Allais Paradox. 3.5 Expected utility and finance. 3.5.1 Traditional valuation 3.5.2 Individual investment and consumption. 3.5.3 Investment and the CAPM. 3.5.4 Portfolio and utility maximization in practice. 3.5.5 Capital markets and the CAPM again. 3.5.6 Stochastic discount factor, assets pricing and the Euler equation. 3.6 Information asymmetry. 3.6.1 ‘The lemon phenomenon’ or adverse selection. 3.6.2 ‘The moral hazard problem’. 3.6.3 Examples of moral hazard. 3.6.4 Signalling and screening. 3.6.5 The principal–agent problem. References and further reading. Chapter 4: Probability and Finance. 4.1 Introduction. 4.2 Uncertainty, games of chance and martingales. 4.3 Uncertainty, random walks and stochastic processes. 4.3.1 The random walk. 4.3.2 Properties of stochastic processes. 4.4 Stochastic calculus. 4.4.1 Ito’s Lemma. 4.5 Applications of Ito’s Lemma. 4.5.1 Applications. 4.5.2 Time discretization of continuous-time finance models. 4.5.3 The Girsanov Theorem and martingales*. References and further reading. Chapter 5: Derivatives Finance. 5.1 Equilibrium valuation and rational expectations. 5.2 Financial instruments. 5.2.1 Forward and futures contracts. 5.2.2 Options. 5.3 Hedging and institutions. 5.3.1 Hedging and hedge funds. 5.3.2 Other hedge funds and investment strategies. 5.3.3 Investor protection rules. References and additional reading. Part II: Mathematical and Computational Finance. Chapter 6: Options and Derivatives Finance Mathematics. 6.1 Introduction to call options valuation. 6.1.1 Option valuation and rational expectations. 6.1.2 Risk-neutral pricing. 6.1.3 Multiple periods with binomial trees. 6.2 Forward and futures contracts. 6.3 Risk-neutral probabilities again. 6.3.1 Rational expectations and optimal forecasts. 6.4 The Black–Scholes options formula. 6.4.1 Options, their sensitivity and hedging parameters. 6.4.2 Option bounds and put–call parity. 6.4.3 American put options. References and additional reading. Chapter 7: Options and Practice. 7.1 Introduction. 7.2 Packaged options. 7.3 Compound options and stock options. 7.3.1 Warrants. 7.3.2 Other options. 7.4 Options and practice. 7.4.1 Plain vanilla strategies. 7.4.2 Covered call strategies: selling a call and a share. 7.4.3 Put and protective put strategies: buying a put and a stock. 7.4.4 Spread strategies. 7.4.5 Straddle and strangle strategies. 7.4.6 Strip and strap strategies. 7.4.7 Butterfly and condor spread strategies. 7.4.8 Dynamic strategies and the Greeks. 7.5 Stopping time strategies*. 7.5.1 Stopping time sell and buy strategies. 7.6 Specific application areas. 7.7 Option misses. References and additional reading. Appendix: First passage time*. Chapter 8: Fixed Income, Bonds and Interest Rates. 8.1 Bonds and yield curve mathematics. 8.1.1 The zero-coupon, default-free bond. 8.1.2 Coupon-bearing bonds. 8.1.3 Net present values (NPV). 8.1.4 Duration and convexity. 8.2 Bonds and forward rates. 8.3 Default bonds and risky debt. 8.4 Rated bonds and default. 8.4.1 A Markov chain and rating. 8.4.2 Bond sensitivity to rates – duration. 8.4.3 Pricing rated bonds and the term structure risk-free rates*. 8.4.4 Valuation of default-prone rated bonds*. 8.5 Interest-rate processes, yields and bond valuation*. 8.5.1 The Vasicek interest-rate model. 8.5.2 Stochastic volatility interest-rate models. 8.5.3 Term structure and interest rates. 8.6 Options on bonds*. 8.6.1 Convertible bonds. 8.6.2 Caps, floors, collars and range notes. 8.6.3 Swaps. References and additional reading. Mathematical appendix. A.1: Term structure and interest rates. A.2: Options on bonds. Chapter 9: Incomplete Markets and Stochastic Volatility. 9.1 Volatility defined. 9.2 Memory and volatility. 9.3 Volatility, equilibrium and incomplete markets. 9.3.1 Incomplete markets. 9.4 Process variance and volatility. 9.5 Implicit volatility and the volatility smile. 9.6 Stochastic volatility models. 9.6.1 Stochastic volatility binomial models*. 9.6.2 Continuous-time volatility models. 9.7 Equilibrium, SDF and the Euler equations*. 9.8 Selected Topics*. 9.8.1 The Hull and White model and stochastic volatility. 9.8.2 Options and jump processes. 9.9 The range process and volatility. References and additional reading. Appendix: Development for the Hull and White model (1987)*. Chapter 10: Value at Risk and Risk Management. 10.1 Introduction. 10.2 VaR definitions and applications. 10.3 VaR statistics. 10.3.1 The historical VaR approach. 10.3.2 The analytic variance–covariance approach. 10.3.3 VaR and extreme statistics. 10.3.4 Copulae and portfolio VaR measurement. 10.3.5 Multivariate risk functions and the principle of maximum entropy. 10.3.6 Monte Carlo simulation and VaR. 10.4 VaR efficiency. 10.4.1 VaR and portfolio risk efficiency with normal returns. 10.4.2 VaR and regret. References and additional reading. Author Index. Subject Index.

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Book SynopsisMissing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches.Trade Review?Overall, this is an excellent text on missing data that is engaging for practitioners while being rigorous enoughfor use in the graduate biostatistics courses.?(Biometrics , September 2009)" "Missing Data in Clinical Studies does an excellent job of presenting essential ideas on modern concepts and techniques relevant to missing data in clinical studies." (Journal of the American Statistician, December 2008) "?this book is reasonably well organized and covers all the relevant theory and much of the practical applications of the field." (Journal of the American Chemical Association, August 6, 2008) "Missing Data in Clinical Studies does an excellent job of presenting essential ideas on modern concepts and techniques relevant to missing data in clinical studies." (Journal of the American Statistician, December 2008) "Clear, generally accessible and well written, and the content is rich. This text is a highly recommendable addition to the shelves of practicing statisticians." (Journal of Applied Statistics, August 2008) "The authors give key examples in the form of several clinical trials and their analyses using the appropriate remedial techniques." (Journal of Tropical Pediatrics, August 2007)Table of ContentsPreface. Acknowledgements. I Preliminaries. 1 Introduction. 1.1 From Imbalance to the Field of Missing Data Research. 1.2 Incomplete Data in Clinical Studies. 1.3 MAR, MNAR, and Sensitivity Analysis. 1.4 Outline of the Book. 2 Key Examples. 2.1 Introduction. 2.2 The Vorozole Study. 2.3 The Orthodontic Growth Data. 2.4 Mastitis in Dairy Cattle. 2.5 The Depression Trials. 2.6 The Fluvoxamine Trial. 2.7 The Toenail Data. 2.8 Age-Related Macular Degeneration Trial. 2.9 The Analgesic Trial. 2.10 The Slovenian Public Opinion Survey. 3 Terminology and Framework. 3.1 Modelling Incompleteness. 3.2 Terminology. 3.3 Missing Data Frameworks. 3.4 Missing Data Mechanisms. 3.5 Ignorability. 3.6 Pattern-Mixture Models. II Classical Techniques and the Need for Modelling. 4 A Perspective on Simple Methods. 4.1 Introduction. 4.2 Simple Methods. 4.3 Problems with Complete Case Analysis and Last Observation Carried Forward. 4.4 Using the Available Cases: a Frequentist versus a Likelihood Perspective. 4.5 Intention to Treat. 4.6 Concluding Remarks. 5 Analysis of the Orthodontic Growth Data. 5.1 Introduction and Models. 5.2 The Original, Complete Data. 5.3 Direct Likelihood. 5.4 Comparison of Analyses. 5.5 Example SAS Code for Multivariate Linear Models. 5.6 Comparative Power under Different Covariance Structures. 5.7 Concluding Remarks. 6 Analysis of the Depression Trials. 6.1 View 1: Longitudinal Analysis. 6.2 Views 2a and 2b and All versus Two Treatment Arms. III Missing at Random and Ignorability. 7 The Direct Likelihood Method. 7.1 Introduction. 7.2 Ignorable Analyses in Practice. 7.3 The Linear Mixed Model. 7.4 Analysis of the Toenail Data. 7.5 The Generalized Linear Mixed Model. 7.6 The Depression Trials. 7.7 The Analgesic Trial. 8 The Expectation–Maximization Algorithm. 8.1 Introduction. 8.2 The Algorithm. 8.3 Missing Information. 8.4 Rate of Convergence. 8.5 EM Acceleration. 8.6 Calculation of Precision Estimates. 8.7 A Simple Illustration. 8.8 Concluding Remarks. 9 Multiple Imputation. 9.1 Introduction. 9.2 The Basic Procedure. 9.3 Theoretical Justification. 9.4 Inference under Multiple Imputation. 9.5 Efficiency. 9.6 Making Proper Imputations. 9.7 Some Roles for Multiple Imputation. 9.8 Concluding Remarks. 10 Weighted Estimating Equations. 10.1 Introduction. 10.2 Inverse Probability Weighting. 10.3 Generalized Estimating Equations for Marginal Models. 10.4 Weighted Generalized Estimating Equations. 10.5 The Depression Trials. 10.6 The Analgesic Trial. 10.7 Double Robustness. 10.8 Concluding Remarks. 11 Combining GEE and MI. 11.1 Introduction. 11.2 Data Generation and Fitting. 11.3 MI-GEE and MI-Transition. 11.4 An Asymptotic Simulation Study. 11.5 Concluding Remarks. 12 Likelihood-Based Frequentist Inference. 12.1 Introduction. 12.2 Information and Sampling Distributions. 12.3 Bivariate Normal Data. 12.4 Bivariate Binary Data. 12.5 Implications for Standard Software. 12.6 Analysis of the Fluvoxamine Trial. 12.7 The Muscatine Coronary Risk Factor Study. 12.8 The Crépeau Data. 12.9 Concluding Remarks. 13 Analysis of the Age-Related Macular Degeneration Trial. 13.1 Introduction. 13.2 Direct Likelihood Analysis of the Continuous Outcome. 13.3 Weighted Generalized Estimating Equations. 13.4 Direct Likelihood Analysis of the Binary Outcome. 13.5 Multiple Imputation. 13.6 Concluding Remarks. 14 Incomplete Data and SAS. 14.1 Introduction. 14.2 Complete Case Analysis. 14.3 Last Observation Carried Forward. 14.4 Direct Likelihood. 14.5 Weighted Estimating Equations. 14.6 Multiple Imputation. IV Missing Not at Random. 15 Selection Models. 15.1 Introduction. 15.2 The Diggle–Kenward Model for Continuous Outcomes. 15.3 Illustration and SAS Implementation. 15.4 An MNAR Dale Model. 15.5 A Model for Non-monotone Missingness. 15.6 Concluding Remarks. 16 Pattern-Mixture Models. 16.1 Introduction. 16.2 A Simple Gaussian Illustration. 16.3 A Paradox. 16.4 Strategies to Fit Pattern-Mixture Models. 16.5 Applying Identifying Restrictions. 16.6 Pattern-Mixture Analysis of the Vorozole Study. 16.7 A Clinical Trial in Alzheimer’s Disease. 16.8 Analysis of the Fluvoxamine Trial. 16.9 Concluding Remarks. 17 Shared-Parameter Models. 18 Protective Estimation. 18.1 Introduction. 18.2 Brown’s Protective Estimator for Gaussian Data. 18.3 A Protective Estimator for Categorical Data. 18.4 A Protective Estimator for Gaussian Data. 18.5 Concluding Remarks. V Sensitivity Analysis. 19 MNAR, MAR, and the Nature of Sensitivity. 19.1 Introduction. 19.2 Every MNAR Model Has an MAR Bodyguard. 19.3 The General Case of Incomplete Contingency Tables. 19.4 The Slovenian Public Opinion Survey. 19.5 Implications for Formal and Informal Model Selection. 19.6 Behaviour of the Likelihood Ratio Test for MAR versus MNAR. 19.7 Concluding Remarks. 20 Sensitivity Happens. 20.1 Introduction. 20.2 A Range of MNAR Models. 20.3 Identifiability Problems. 20.4 Analysis of the Fluvoxamine Trial. 20.5 Concluding Remarks. 21 Regions of Ignorance and Uncertainty. 21.1 Introduction. 21.2 Prevalence of HIV in Kenya. 21.3 Uncertainty and Sensitivity. 21.4 Models for Monotone Patterns. 21.5 Models for Non-monotone Patterns. 21.6 Formalizing Ignorance and Uncertainty. 21.7 Analysis of the Fluvoxamine Trial. 21.8 Artificial Examples. 21.9 The Slovenian Public Opinion Survey. 21.10 Some Theoretical Considerations. 21.11 Concluding Remarks. 22 Local and Global Influence Methods. 22.1 Introduction. 22.2 Gaussian Outcomes. 22.3 Mastitis in Dairy Cattle. 22.4 Alternative Local Influence Approaches. 22.5 The Milk Protein Content Trial. 22.6 Analysis of the Depression Trials. 22.7 A Local Influence Approach for Ordinal Data with Dropout. 22.8 Analysis of the Fluvoxamine Data. 22.9 A Local Influence Approach for Incomplete Binary Data. 22.10 Analysis of the Fluvoxamine Data. 22.11 Concluding Remarks. 23 The Nature of Local Influence. 23.1 Introduction. 23.2 The Rats Data. 23.3 Analysis and Sensitivity Analysis of the Rats Data. 23.4 Local Influence Methods and Their Behaviour. 23.5 Concluding Remarks. 24 A Latent-Class Mixture Model for Incomplete Longitudinal Gaussian Data. 24.1 Introduction. 24.2 Latent-Class Mixture Models. 24.3 The Likelihood Function and Estimation. 24.4 Classification. 24.5 Simulation Study. 24.6 Analysis of the Depression Trials. 24.7 Concluding Remarks. VI Case Studies. 25 The Age-Related Macular Degeneration Trial. 25.1 Selection Models and Local Influence. 25.2 Local Influence Analysis. 25.3 Pattern-Mixture Models. 25.4 Concluding Remarks. 26 The Vorozole Study. 26.1 Introduction. 26.2 Exploring the Vorozole Data. 26.3 A Selection Model for the Vorozole Study. 26.4 A Pattern-Mixture Model for the Vorozole Study. 26.5 Concluding Remarks. References. Index.

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Book SynopsisThe increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. This book covers the subject.Trade Review"I liked this book and would recommend it to any statistician new to microarray data analysis…a unique combination of features that make it a contender among the standard textbooks…" (Journal of the American Statistical Association, June 2006) "...clear...up-to-date...lively advice...an excellent reference text for any researcher interested in the analysis of transcriptomic data." (Short Book Reviews, Vol.25, No.1, April 2005) "...this is a very good introduction to one of the most widely used methods for assessing differential expression..." (Journal of the Royal Statistical Society, Vol 168 (4) 2005) "...presents a coherent and systematic overview of statistical methods in all stages of the process of analysing microarray data..." (Zentralblatt Math, Vol.1049, 2004)Table of ContentsPreface. 1 Preliminaries. 1.1 Using the R Computing Environment. 1.1.1 Installing smida. 1.1.2 Loading smida. 1.2 Data Sets from Biological Experiments. 1.2.1 Arabidopsis experiment: Anna Amtmann. 1.2.2 Skin cancer experiment: Nighean Barr. 1.2.3 Breast cancer experiment: John Bartlett. 1.2.4 Mammary gland experiment: Gusterson group. 1.2.5 Tuberculosis experiment: BµG@S group. I Getting Good Data. 2 Set-up of a Microarray Experiment. 2.1 Nucleic Acids: DNA and RNA. 2.2 Simple cDNA Spotted Microarray Experiment. 2.2.1 Growing experimental material. 2.2.2 Obtaining RNA. 2.2.3 Adding spiking RNA and poly-T primer. 2.2.4 Preparing the enzyme environment. 2.2.5 Obtaining labelled cDNA. 2.2.6 Preparing cDNA mixture for hybridization. 2.2.7 Slide hybridization. 3 Statistical Design of Microarrays. 3.1 Sources of Variation. 3.2 Replication. 3.2.1 Biological and technical replication. 3.2.2 How many replicates? 3.2.3 Pooling samples. 3.3 Design Principles. 3.3.1 Blocking, crossing and randomization. 3.3.2 Design and normalization. 3.4 Single-channelMicroarray Design. 3.4.1 Design issues. 3.4.2 Design layout. 3.4.3 Dealing with technical replicates. 3.5 Two-channelMicroarray Designs. 3.5.1 Optimal design of dual-channel arrays. 3.5.2 Several practical two-channel designs. 4 Normalization. 4.1 Image Analysis. 4.1.1 Filtering. 4.1.2 Gridding. 4.1.3 Segmentation. 4.1.4 Quantification. 4.2 Introduction to Normalization. 4.2.1 Scale of gene expression data. 4.2.2 Using control spots for normalization. 4.2.3 Missing data. 4.3 Normalization for Dual-channel Arrays. 4.3.1 Order for the normalizations. 4.3.2 Spatial correction. 4.3.3 Background correction. 4.3.4 Dye effect normalization. 4.3.5 Normalization within and across conditions. 4.4 Normalization of Single-channel Arrays. 4.4.1 Affymetrix data structure. 4.4.2 Normalization of Affymetrix data. 5 Quality Assessment. 5.1 Using MIAME in Quality Assessment. 5.1.1 Components of MIAME. 5.2 Comparing Multivariate Data. 5.2.1 Measurement scale. 5.2.2 Dissimilarity and distance measures. 5.2.3 Representing multivariate data. 5.3 Detecting Data Problems. 5.3.1 Clerical errors. 5.3.2 Normalization problems. 5.3.3 Hybridization problems. 5.3.4 Array mishandling. 5.4 Consequences of Quality Assessment Checks. 6 Microarray Myths: Data. 6.1 Design. 6.1.1 Single-versus dual-channel designs? 6.1.2 Dye-swap experiments. 6.2 Normalization. 6.2.1 Myth: ‘microarray data is Gaussian’. 6.2.2 Myth: ‘microarray data is not Gaussian’. 6.2.3 Confounding spatial and dye effect. 6.2.4 Myth: ‘non-negative background subtraction’. II Getting Good Answers. 7 Microarray Discoveries. 7.1 Discovering Sample Classes. 7.1.1 Why cluster samples? 7.1.2 Sample dissimilarity measures. 7.1.3 Clustering methods for samples. 7.2 Exploratory Supervised Learning. 7.2.1 Labelled dendrograms. 7.2.2 Labelled PAM-type clusterings. 7.3 Discovering Gene Clusters. 7.3.1 Similarity measures for expression profiles. 7.3.2 Gene clustering methods. 8 Differential Expression. 8.1 Introduction. 8.1.1 Classical versus Bayesian hypothesis testing. 8.1.2 Multiple testing ‘problem’. 8.2 Classical Hypothesis Testing. 8.2.1 What is a hypothesis test? 8.2.2 Hypothesis tests for two conditions. 8.2.3 Decision rules. 8.2.4 Results from skin cancer experiment. 8.3 Bayesian Hypothesis Testing. 8.3.1 A general testing procedure. 8.3.2 Bayesian t-test. 9 Predicting Outcomes with Gene Expression Profiles. 9.1 Introduction. 9.1.1 Probabilistic classification theory. 9.1.2 Modelling and predicting continuous variables. 9.2 Curse of Dimensionality: Gene Filtering. 9.2.1 Use only significantly expressed genes. 9.2.2 PCA and gene clustering. 9.2.3 Penalized methods. 9.2.4 Biological selection. 9.3 Predicting ClassMemberships. 9.3.1 Variance-bias trade-off in prediction. 9.3.2 Linear discriminant analysis. 9.3.3 k-nearest neighbour classification. 9.4 Predicting Continuous Responses. 9.4.1 Penalized regression: LASSO. 9.4.2 k-nearest neighbour regression. 10 Microarray Myths: Inference. 10.1 Differential Expression. 10.1.1 Myth: ‘Bonferroni is too conservative’. 10.1.2 FPR and collective multiple testing. 10.1.3 Misinterpreting FDR. 10.2 Prediction and Learning. 10.2.1 Cross-validation. Bibliography. Index.

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Book SynopsisGroundwater is a vital source of water throughout the world. As the number of groundwater investigations increase, it is important to understand how to develop comprehensive quantified conceptual models and appreciate the basis of analytical solutions or numerical methods of modelling groundwater flow. Groundwater Hydrology: Conceptual and Computational Models describes advances in both conceptual and numerical modelling. It gives insights into the interpretation of field information, the development of conceptual models, the use of computational models based on analytical and numerical techniques, the assessment of the adequacy of models, and the use of computational models for predictive purposes. It focuses on the study of groundwater flow problems and a thorough analysis of real practical field case studies. It is divided into three parts: * Part I deals with the basic principles, including a summary of mathematical descriptions of groundwater flow, recharge estimTrade Review"...well written and structured...a comprehensive and thorough reference source...highly recommended for anyone in the business..." (Circulation - N'ltr of British Hydrological Soc, Feb 2004) "...delighted to have this book on my shelf and it is already becoming well thumbed...no hesitation in recommending it..." (Geoscientist, May 2004) "The information and techniques presented in this book provide illuminating guidelines and application directions for practicing hydrogeologists, geohydrologists and water resource engineers." (Hydrological Sciences Journal, Feb 2005, Vol 50 (1))Table of ContentsPreface. 1. Introduction. PART I: BASIC PRINCIPLES. 2. Background to Groundwater Flow. 3. Recharge due to Precipitation or Irrigation. 4. Interaction between Surface Water and Groundwater. PART II: RADIAL FLOW. 5. Radial Flow to Pumped Boreholes – Fundamental Issues. 6. Large Diameter Wells. 7. Radial Flow where Vertical Components of Flow are Significant. 8. Practical Issues of Interpretation and Assessing Resources. PART III: REGIONAL GROUNDWATER FLOW. 9. Regional Groundwater Studies in which Transmissivity is Effectively Constant. 10. Regional Groundwater Flow in Multi-Aquifer Systems. 11. Regional Groundwater Flow with Hydraulic Conductivity Varying with Saturated Thickness. 12. Numerical Modelling Insights. Appendix: Computer Program for Two-zone Model. List of Symbols. References. Index.

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Book SynopsisFeatures coverage of the epidemiological indices, and includes developments in the field. This title serves as a useful reference source for biostatisticians and epidemiologists working in disease prevention, as the chapters are self-contained and feature several real examples.Trade Review"…a concise, organized, and well-written text that provides the derivations of statistical formulas underlying much epidemiological research and practice." (Journal of the American Statistical Association, December 2005) "...presents a considerable amount of recent research, much of which is the author's own..." (Royal Statistical Society, Vol.168, No.1, January 2005) "...systematically organised...an excellent reference..." (Short Book Review, Vol.24, No.3 December 2004) "...this book is strongly recommended..." (Statistical Methods in Medical Research, Vol 14 2005)Table of ContentsAbout the author. Preface. 1 Population Proportion or Prevalence. 1.1 Binomial sampling. 1.2 Cluster sampling. 1.3 Inverse sampling. Exercises. References. 2 Risk Difference. 2.1 Independent binomial sampling. 2.2 A series of independent binomial sampling procedures. 2.2.1 Summary interval estimators. 2.2.2 Test for the homogeneity of risk difference. 2.3 Independent cluster sampling. 2.4 Paired-sample data. 2.5 Independent negative binomial sampling (inverse sampling). 2.6 Independent poisson sampling. 2.7 Stratified poisson sampling. Exercises. References. 3 Relative Difference. 3.1 Independent binomial sampling. 3.2 A series of independent binomial sampling procedures. 3.2.1 Asymptotic interval estimators. 3.2.2 Test for the homogeneity of relative difference. 3.3 Independent cluster sampling. 3.4 Paired-sample data. 3.5 Independent inverse sampling. Exercises. References. 4 Relative Risk. 4.1 Independent binomial sampling. 4.2 A series of independent binomial sampling procedures. 4.2.1 Asymptotic interval estimators. 4.2.2 Test for the homogeneity of risk ratio. 4.3 Independent cluster sampling. 4.4 Paired-sample data. 4.5 Independent inverse sampling. 4.5.1 Uniformly minimum variance unbiased estimator of relative risk. 4.5.2 Interval estimators of relative risk. 4.6 Independent poisson sampling. 4.7 Stratified poisson sampling. Exercises. References. 5 Odds Ratio. 5.1 Independent binomial sampling. 5.1.1 Asymptotic interval estimators. 5.1.2 Exact confidence interval. 5.2 A series of independent binomial sampling procedures. 5.2.1 Asymptotic interval estimators. 5.2.2 Exact confidence interval. 5.2.3 Test for homogeneity of the odds ratio. 5.3 Independent cluster sampling. 5.4 One-to-one matched sampling. 5.5 Logistic modeling. 5.5.1 Estimation under multinomial or independent binomial sampling. 5.5.2 Estimation in the case of paired-sample data. 5.6 Independent inverse sampling. 5.7 Negative multinomial sampling for paired-sample data. Exercises. References. 6 Generalized Odds Ratio. 6.1 Independent multinomial sampling. 6.2 Data with repeated measurements (or under cluster sampling). 6.3 Paired-sample data. 6.4 Mixed negative multinomial and multinomial sampling. Exercises. References. 7 Attributable Risk. 7.1 Study designs with no confounders. 7.1.1 Cross-sectional sampling. 7.1.2 Case–control studies. 7.2 Study designs with confounders. 7.2.1 Cross-sectional sampling. 7.2.2 Case–control studies. 7.3 Case–control studies with matched pairs. 7.4 Multiple levels of exposure in case–control studies. 7.5 Logistic modeling in case–control studies. 7.5.1 Logistic model containing only the exposure variables of interest. 7.5.2 Logistic regression model containing both exposure and confounding variables. 7.6 Case–control studies under inverse sampling. Exercises. References. 8 Number Needed to Treat. 8.1 Independent binomial sampling. 8.2 A series of independent binomial sampling procedures. 8.3 Independent cluster sampling. 8.4 Paired-sample data. Exercises. References. Appendix Maximum Likelihood Estimator and Large-Sample Theory. A.1: The maximum likelihood estimator, Wald’s test, the score test, and the asymptotic likelihood ratio test. A.2: The delta method and its applications. References. Answers to Selected Exercises. Index.

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Book SynopsisPricing is a very complex subject and in practice depends on many parameters of the actual market including amount of traffic, architecture of the network, technology, and cost. This book provides a framework of mathematical models for pricing multidimensional contracts. It also provides an overview of network services and contracts.Table of ContentsPreface. List of Acronyms. A: NETWORKS. Pricing and Communications Networks. Network Services and Contracts. Network Technology. Network Constraints and Effective Bandwidths. B: ECONOMICS. Basic Concepts. Competition Models. C: PRICING. Cost-based Pricing. Charging Guaranteed Services. Congestion. Charging Flexible Contracts. D: SPECIAL TOPICS. Multicasting. Interconnection. Regulation. Auctions. Appendix A: Lagrangian Methods for Constrained Optimization. Appendix B: Convergence of Tatonnement. References. Index.

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Book SynopsisFinancial mathematics has recently enjoyed considerable interest on account of its impact on the finance industry. In parallel, the theory of Levy processes has also seen many exciting developments. These powerful modelling tools allow the user to model more complex phenomena, and are commonly applied to problems in finance.Table of ContentsPreface. Acknowledgements. Introduction. Financial Mathematics in Continuous Time. The Black-Scholes Model. Imperfections of the Black-Scholes Model. Lévy Processes and OU Processes. Stock Price Models Driven by Lévy Processes. Lévy Models with Stochastic Volatility. Simulation Techniques. Exotic Option Pricing. Interest-Rate Models. Appendix A: Special Functions. Appendix B: Lévy Processes. Appendix C: S&P 500 Call Option Prices. References. Index.

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Book SynopsisSimulation and Monte Carlo is aimed at students studying for degrees in Mathematics, Statistics, Financial Mathematics, Operational Research, Computer Science, and allied subjects, who wish an up-to-date account of the theory and practice of Simulation. Its distinguishing features are in-depth accounts of the theory of Simulation, including the important topic of variance reduction techniques, together with illustrative applications in Financial Mathematics, Markov chain Monte Carlo, and Discrete Event Simulation. Each chapter contains a good selection of exercises and solutions with an accompanying appendix comprising a Maple worksheet containing simulation procedures. The worksheets can also be downloaded from the web site supporting the book. This encourages readers to adopt a hands-on approach in the effective design of simulation experiments. Arising from a course taught at Edinburgh University over several years, the book will also appeal to practitioners working in thTrade Review?This book would be immensely useful for any practitioner seeking to learn more about this field, as well as for lecturers seeking a reliable and informative text.? ( Significance, September 2009) "The book does a nice job of discussing, developing, and presenting the mathematical aspects of random processes, random number generation, and Markov chain Monte Carlo (MCMC) methods. I particularly like the notation used and the depth of proofs offered; they are technically correct, well organized, and nicely presented." (Journal of the American Statistical Association, June 2008) ?Dagpunar presents a textbook based on 20-hour courses he has taught for advanced students of mathematics and students of financial mathematics.? (SciTech Book Reviews, June 2007) "?excellent for students and practitioners who don't have previous experience with simulation methods?a great contribution." (MAA Reviews, April 5, 2007)Table of ContentsPreface. Glossary. 1 Introduction to simulation and Monte Carlo. 1.1 Evaluating a definite integral. 1.2 Monte Carlo is integral estimation. 1.3 An example. 1.4 A simulation using Maple. 1.5 Problems. 2 Uniform random numbers. 2.1 Linear congruential generators. 2.2 Theoretical tests for random numbers. 2.3 Shuffled generator. 2.4 Empirical tests. 2.5 Combinations of generators. 2.6 The seed(s) in a random number generator. 2.7 Problems. 3 General methods for generating random variates. 3.1 Inversion of the cumulative distribution function. 3.2 Envelope rejection. 3.3 Ratio of uniforms method. 3.4 Adaptive rejection sampling. 3.5 Problems. 4 Generation of variates from standard distributions. 4.1 Standard normal distribution. 4.2 Lognormal distribution. 4.3 Bivariate normal density. 4.4 Gamma distribution. 4.5 Beta distribution. 4.6 Chi-squared distribution. 4.7 Student’s t distribution. 4.8 Generalized inverse Gaussian distribution. 4.9 Poisson distribution. 4.10 Binomial distribution. 4.11 Negative binomial distribution. 4.12 Problems. 5 Variance reduction. 5.1 Antithetic variates. 5.2 Importance sampling. 5.3 Stratified sampling. 5.4 Control variates. 5.5 Conditional Monte Carlo. 5.6 Problems. 6 Simulation and finance. 6.1 Brownian motion. 6.2 Asset price movements. 6.3 Pricing simple derivatives and options. 6.4 Asian options. 6.5 Basket options. 6.6 Stochastic volatility. 6.7 Problems. 7 Discrete event simulation. 7.1 Poisson process. 7.2 Time-dependent Poisson process. 7.3 Poisson processes in the plane. 7.4 Markov chains. 7.5 Regenerative analysis. 7.6 Simulating a G/G/1 queueing system using the three-phase method. 7.7 Simulating a hospital ward. 7.8 Problems. 8 Markov chain Monte Carlo. 8.1 Bayesian statistics. 8.2 Markov chains and the Metropolis–Hastings (MH) algorithm. 8.3 Reliability inference using an independence sampler. 8.4 Single component Metropolis–Hastings and Gibbs sampling. 8.5 Other aspects of Gibbs sampling. 8.6 Problems. 9 Solutions. 9.1 Solutions 1. 9.2 Solutions 2. 9.3 Solutions 3. 9.4 Solutions 4. 9.5 Solutions 5. 9.6 Solutions 6. 9.7 Solutions 7. 9.8 Solutions 8. Appendix 1: Solutions to problems in Chapter 1. Appendix 2: Random Number Generators. Appendix 3: Computations of acceptance probabilities. Appendix 4: Random variate generators (standard distributions). Appendix 5: Variance Reduction. Appendix 6: Simulation and Finance. Appendix 7: Discrete event simulation. Appendix 8: Markov chain Monte Carlo. References. Index.

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Book SynopsisSimulation and Monte Carlo is aimed at students studying for degrees in Mathematics, Statistics, Financial Mathematics, Operational Research, Computer Science, and allied subjects, who wish an up-to-date account of the theory and practice of Simulation. Its distinguishing features are in-depth accounts of the theory of Simulation, including the important topic of variance reduction techniques, together with illustrative applications in Financial Mathematics, Markov chain Monte Carlo, and Discrete Event Simulation. Each chapter contains a good selection of exercises and solutions with an accompanying appendix comprising a Maple worksheet containing simulation procedures. The worksheets can also be downloaded from the web site supporting the book. This encourages readers to adopt a hands-on approach in the effective design of simulation experiments. Arising from a course taught at Edinburgh University over several years, the book will also appeal to practitioners working in thTrade Review?This book would be immensely useful for any practitioner seeking to learn more about this field, as well as for lecturers seeking a reliable and informative text.? ( Significance, September 2009) "The book does a nice job of discussing, developing, and presenting the mathematical aspects of random processes, random number generation, and Markov chain Monte Carlo (MCMC) methods. I particularly like the notation used and the depth of proofs offered; they are technically correct, well organized, and nicely presented." (Journal of the American Statistical Association, June 2008) ?Dagpunar presents a textbook based on 20-hour courses he has taught for advanced students of mathematics and students of financial mathematics.? (SciTech Book Reviews, June 2007) "?excellent for students and practitioners who don't have previous experience with simulation methods?a great contribution." (MAA Reviews, April 5, 2007)Table of ContentsPreface. Glossary. 1 Introduction to simulation and Monte Carlo. 1.1 Evaluating a definite integral. 1.2 Monte Carlo is integral estimation. 1.3 An example. 1.4 A simulation using Maple. 1.5 Problems. 2 Uniform random numbers. 2.1 Linear congruential generators. 2.2 Theoretical tests for random numbers. 2.3 Shuffled generator. 2.4 Empirical tests. 2.5 Combinations of generators. 2.6 The seed(s) in a random number generator. 2.7 Problems. 3 General methods for generating random variates. 3.1 Inversion of the cumulative distribution function. 3.2 Envelope rejection. 3.3 Ratio of uniforms method. 3.4 Adaptive rejection sampling. 3.5 Problems. 4 Generation of variates from standard distributions. 4.1 Standard normal distribution. 4.2 Lognormal distribution. 4.3 Bivariate normal density. 4.4 Gamma distribution. 4.5 Beta distribution. 4.6 Chi-squared distribution. 4.7 Student’s t distribution. 4.8 Generalized inverse Gaussian distribution. 4.9 Poisson distribution. 4.10 Binomial distribution. 4.11 Negative binomial distribution. 4.12 Problems. 5 Variance reduction. 5.1 Antithetic variates. 5.2 Importance sampling. 5.3 Stratified sampling. 5.4 Control variates. 5.5 Conditional Monte Carlo. 5.6 Problems. 6 Simulation and finance. 6.1 Brownian motion. 6.2 Asset price movements. 6.3 Pricing simple derivatives and options. 6.4 Asian options. 6.5 Basket options. 6.6 Stochastic volatility. 6.7 Problems. 7 Discrete event simulation. 7.1 Poisson process. 7.2 Time-dependent Poisson process. 7.3 Poisson processes in the plane. 7.4 Markov chains. 7.5 Regenerative analysis. 7.6 Simulating a G/G/1 queueing system using the three-phase method. 7.7 Simulating a hospital ward. 7.8 Problems. 8 Markov chain Monte Carlo. 8.1 Bayesian statistics. 8.2 Markov chains and the Metropolis–Hastings (MH) algorithm. 8.3 Reliability inference using an independence sampler. 8.4 Single component Metropolis–Hastings and Gibbs sampling. 8.5 Other aspects of Gibbs sampling. 8.6 Problems. 9 Solutions. 9.1 Solutions 1. 9.2 Solutions 2. 9.3 Solutions 3. 9.4 Solutions 4. 9.5 Solutions 5. 9.6 Solutions 6. 9.7 Solutions 7. 9.8 Solutions 8. Appendix 1: Solutions to problems in Chapter 1. Appendix 2: Random Number Generators. Appendix 3: Computations of acceptance probabilities. Appendix 4: Random variate generators (standard distributions). Appendix 5: Variance Reduction. Appendix 6: Simulation and Finance. Appendix 7: Discrete event simulation. Appendix 8: Markov chain Monte Carlo. References. Index.

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Book SynopsisReliability & Risk: A Bayesian Perspective addresses the need for a sound introduction to the mathematical and statistical aspects of reliability analysis from a Bayesian perspective. It features many real examples, taken from the author's vast experience, and lots of applications from reliability engineering.Trade Review"The book is written by an expert in reliability analysis and it is a very valuable source of information for mathematical models for reliability problems ... An extensive bibliography concludes the book." (Stat Papers, 2011) "As the author mentions in his preface, the book can be read in several different ways, as a text for a graduate level course on reliability or as a source book for “information and open problems." This book has been a joy to read for this reviewer." (International Statistical Review, August 2008) "Singpurwalla seems to be at his best in probabilistic modeling of reality. He has written what must be one of the first books reliability written from a subjective, Bayesian point of view." (International Statistical Review, August 2008) "The material of this book will be most profitable for practitioners and researchers in reliability and survivability, who will greatly appreciate it as a source of information and open problems." (Mathematical Reviews, 2008h) "This is a very interesting, provocative, and worthwhile book." (Biometrics, June 2008) "What I liked most about this book, however, is the way it blends interesting technical material with foundational discussion about the nature of uncertainty." (Biometrics, June 2008) "The investigation of the theoretical models under consideration in the book is first class…" (Law, Probability and Risk Advance Access, September 2007) "I feel that I have learned an effective plotting technique from these plots…" (Technometrics, February 2008) "…a cornucopia of probability models and inference methods for different problems…[that] serve as a rich taxonomy that statisticians can use to fit models…works as both an educational tool and as a reference." (MAA Reviews, March 6, 2007)Table of ContentsPreface xiii Acknowledgements xv 1 Introduction and Overview 1 1.1 Preamble: What do ‘Reliability’, ‘Risk’ and ‘Robustness’ Mean? 1 1.2 Objectives and Prospective Readership 3 1.3 Reliability, Risk and Survival: State-of-the-Art 3 1.4 Risk Management: A Motivation for Risk Analysis 4 1.5 Books on Reliability, Risk and Survival Analysis 6 1.6 Overview of the Book 7 2 The Quantification of Uncertainty 9 2.1 Uncertain Quantities and Uncertain Events: Their Definition and Codification 9 2.2 Probability: A Satisfactory Way to Quantify Uncertainty 10 2.2.1 The Rules of Probability 11 2.2.2 Justifying the Rules of Probability 12 2.3 Overview of the Different Interpretations of Probability 13 2.3.1 A Brief History of Probability 14 2.3.2 The Different Kinds of Probability 16 2.4 Extending the Rules of Probability: Law of Total Probability and Bayes’ Law 19 2.4.1 Marginalization 20 2.4.2 The Law of Total Probability 20 2.4.3 Bayes’ Law: The Incorporation of Evidence and the Likelihood 20 2.5 The Bayesian Paradigm: A Prescription for Reliability, Risk and Survival Analysis 22 2.6 Probability Models, Parameters, Inference and Prediction 23 2.6.1 The Genesis of Probability Models and Their Parameters 24 2.6.2 Statistical Inference and Probabilistic Prediction 26 2.7 Testing Hypotheses: Posterior Odds and Bayes Factors 27 2.7.1 Bayes Factors: Weight of Evidence and Change in Odds 28 2.7.2 Uses of the Bayes Factor 30 2.7.3 Alternatives to Bayes Factors 31 2.8 Utility as Probability and Maximization of Expected Utility 32 2.8.1 Utility as a Probability 32 2.8.2 Maximization of Expected Utility 33 2.8.3 Attitudes to Risk: The Utility of Money 33 2.9 Decision Trees and Influence Diagrams for Risk Analysis 34 2.9.1 The Decision Tree 34 2.9.2 The Influence Diagram 35 3 Exchangeability and Indifference 45 3.1 Introduction to Exchangeability: de Finetti’s Theorem 45 3.1.1 Motivation for the Judgment of Exchangeability 46 3.1.2 Relationship between Independence and Exchangeability 46 3.1.3 de Finetti’s Representation Theorem for Zero-one Exchangeable Sequences 48 3.1.4 Exchangeable Sequences and the Law of Large Numbers 49 3.2 de Finetti-style Theorems for Infinite Sequences of Non-binary Random Quantities 50 3.2.1 Sufficiency and Indifference in Zero-one Exchangeable Sequences 51 3.2.2 Invariance Conditions Leading to Mixtures of Other Distributions 51 3.3 Error Bounds on de Finetti-style Results for Finite Sequences of Random Quantities 55 3.3.1 Bounds for Finitely Extendable Zero-one Random Quantities 55 3.3.2 Bounds for Finitely Extendable Non-binary Random Quantities 56 4 Stochastic Models of Failure 59 4.1 Introduction 59 4.2 Preliminaries: Univariate, Multivariate and Multi-indexed Distribution Functions 59 4.3 The Predictive Failure Rate Function of a Univariate Probability Distribution 62 4.3.1 The Case of Discontinuity 65 4.4 Interpretation and Uses of the Failure Rate Function – the Model Failure Rate 66 4.4.1 The True Failure Rate: Does it Exist? 69 4.4.2 Decreasing Failure Rates, Reliability Growth, Burn-in and the Bathtub Curve 69 4.4.3 The Retrospective (or Reversed) Failure Rate 74 4.5 Multivariate Analogues of the Failure Rate Function 76 4.5.1 The Hazard Gradient 76 4.5.2 The Multivariate Failure Rate Function 77 4.5.3 The Conditional Failure Rate Functions 78 4.6 The Hazard Potential of Items and Individuals 79 4.6.1 Hazard Potentials and Dependent Lifelengths 81 4.6.2 The Hazard Gradient and Conditional Hazard Potentials 83 4.7 Probability Models for Interdependent Lifelengths 85 4.7.1 Preliminaries: Bivariate Distributions 85 4.7.2 The Bivariate Exponential Distributions of Gumbel 89 4.7.3 Freund’s Bivariate Exponential Distribution 91 4.7.4 The Bivariate Exponential of Marshall and Olkin 93 4.7.5 The Bivariate Pareto as a Failure Model 107 4.7.6 A Bivariate Exponential Induced by a Shot-noise Process 110 4.7.7 A Bivariate Exponential Induced by a Bivariate Pareto’s Copula 115 4.7.8 Other Specialized Bivariate Distributions 115 4.8 Causality and Models for Cascading Failures 117 4.8.1 Probabilistic Causality and Causal Failures 117 4.8.2 Cascading and Models of Cascading Failures 118 4.9 Failure Distributions with Multiple Scales 120 4.9.1 Model Development 120 4.9.2 A Failure Model Indexed by Two Scales 123 5 Parametric Failure Data Analysis 125 5.1 Introduction and Perspective 125 5.2 Assessing Predictive Distributions in the Absence of Data 127 5.2.1 The Exponential as a Chance Distribution 127 5.2.2 The Weibull (and Gamma) as a Chance Distribution 128 5.2.3 The Bernoulli as a Chance Distribution 129 5.2.4 The Poisson as a Chance Distribution 133 5.2.5 The Generalized Gamma as a Chance Distribution 135 5.2.6 The Inverse Gaussian as a Chance Distribution 136 5.3 Prior Distributions in Chance Distributions 136 5.3.1 Eliciting Prior Distributions via Expert Testimonies 137 5.3.2 Using Objective (or Default) Priors 141 5.4 Predictive Distributions Incorporating Failure Data 144 5.4.1 Design Strategies for Industrial Life-testing 145 5.4.2 Stopping Rules: Non-informative and Informative 147 5.4.3 The Total Time on Test 149 5.4.4 Exponential Life-testing Procedures 150 5.4.5 Weibull Life-testing Procedures 155 5.4.6 Life-testing Under the Generalized Gamma and the Inverse Gaussian 156 5.4.7 Bernoulli Life-testing Procedures 157 5.4.8 Life-testing and Inference Under the BVE 159 5.5 Information from Life-tests: Learning from Data 161 5.5.1 Preliminaries: Entropy and Information 161 5.5.2 Learning for Inference from Life-test Data: Testing for Confidence 164 5.5.3 Life-testing for Decision Making: Acceptance Sampling 166 5.6 Optimal Testing: Design of Life-testing Experiments 170 5.7 Adversarial Life-testing and Acceptance Sampling 173 5.8 Accelerated Life-testing and Dose–response Experiments 175 5.8.1 Formulating Accelerated Life-testing Problems 175 5.8.2 The Kalman Filter Model for Prediction and Smoothing 177 5.8.3 Inference from Accelerated Tests Using the Kalman Filter 179 5.8.4 Designing Accelerated Life-testing Experiments 183 6 Composite Reliability: Signatures 187 6.1 Introduction: Hierarchical Models 187 6.2 ‘Composite Reliability’: Partial Exchangeability 188 6.2.1 Simulating Exchangeable and Partially Exchangeable Sequences 189 6.2.2 The Composite Reliability of Ultra-reliable Units 190 6.2.3 Assessing Reliability and Composite Reliability 192 6.3 Signature Analysis and Signatures as Covariates 193 6.3.1 Assessing the Power Spectrum via a Regression Model 195 6.3.2 Bayesian Assessment of the Power Spectrum 195 6.3.3 A Hierarchical Bayes Assessment of the Power Spectrum 198 6.3.4 The Spectrum as a Covariate Using an Accelerated Life Model 200 6.3.5 Closing Remarks on Signatures and Covariates 202 7 Survival in Dynamic Environments 205 7.1 Introduction: Why Stochastic Hazard Functions? 205 7.2 Hazard Rate Processes 206 7.2.1 Hazard Rates as Shot-noise Processes 207 7.2.2 Hazard Rates as Lévy Processes 208 7.2.3 Hazard Rates as Functions of Diffusion Processes 210 7.3 Cumulative Hazard Processes 211 7.3.1 The Cumulative Hazard as a Compound Poisson Process 213 7.3.2 The Cumulative Hazard as an Increasing Lévy Process 213 7.3.3 Cumulative Hazard as Geometric Brownian Motion 214 7.3.4 The Cumulative Hazard as a Markov Additive Process 215 7.4 Competing Risks and Competing Risk Processes 218 7.4.1 Deterministic Competing Risks 219 7.4.2 Stochastic Competing Risks and Competing Risk Processes 220 7.5 Degradation and Aging Processes 222 7.5.1 A Probabilistic Framework for Degradation Modeling 223 7.5.2 Specifying Degradation Processes 223 8 Point Processes for Event Histories 227 8.1 Introduction: What is Event History? 227 8.1.1 Parameterizing the Intensity Function 229 8.2 Other Point Processes in Reliability and Life-testing 229 8.2.1 Multiple Failure Modes and Competing Risks 229 8.2.2 Items Experiencing Degradation and Deterioration 231 8.2.3 Units Experiencing Maintenance and Repair 231 8.2.4 Life-testing Under Censorship and Withdrawals 233 8.3 Multiplicative Intensity and Multivariate Point Processes 234 8.3.1 Multivariate Counting and Intensity Processes 234 8.4 Dynamic Processes and Statistical Models: Martingales 236 8.4.1 Decomposition of Continuous Time Processes 238 8.4.2 Stochastic Integrals and a Martingale Central Limit Theorem 239 8.5 Point Processes with Multiplicative Intensities 240 9 Non-parametric Bayes Methods in Reliability 243 9.1 The What and Why of Non-parametric Bayes 243 9.2 The Dirichlet Distribution and its Variants 244 9.2.1 The Ordered Dirichlet Distribution 246 9.2.2 The Generalized Dirichlet – Concept of Neutrality 246 9.3 A Non-parametric Bayes Approach to Bioassay 247 9.3.1 A Prior for Potency 248 9.3.2 The Posterior Potency 249 9.4 Prior Distributions on the Hazard Function 250 9.4.1 Independent Beta Priors on Piecewise Constant Hazards 250 9.4.2 The Extended Gamma Process as a Prior 251 9.5 Prior Distributions for the Cumulative Hazard Function 253 9.5.1 Neutral to the Right Probabilities and Gamma Process Priors 253 9.5.2 Beta Process Priors for the Cumulative Hazard 255 9.6 Priors for the Cumulative Distribution Function 259 9.6.1 The Dirichlet Process Prior 260 9.6.2 Neutral to the Right-prior Processes 264 10 Survivability of Co-operative, Competing and Vague Systems 269 10.1 Introduction: Notion of Systems and their Components 269 10.1.1 Overview of the Chapter 269 10.2 Coherent Systems and their Qualitative Properties 270 10.2.1 The Reliability of Coherent Systems 274 10.3 The Survivability of Coherent Systems 281 10.3.1 Performance Processes and their Driving Processes 282 10.3.2 System Survivability Under Hierarchical Independence 283 10.3.3 System Survivability Under Interdependence 284 10.3.4 Prior Distributions on the Unit Hypercube 286 10.4 Machine Learning Methods in Survivability Assessment 291 10.4.1 An Overview of the Neural Net Methodology 292 10.4.2 A Two-phased Neural Net for System Survivability 293 10.5 Reliability Allocation: Optimal System Design 294 10.5.1 The Decision Theoretic Formulation 294 10.5.2 Reliability Apportionment for Series Systems 296 10.5.3 Reliability Apportionment for Parallel Redundant Systems 297 10.5.4 Apportioning Node Reliabilities in Networks 298 10.5.5 Apportioning Reliability Under Interdependence 298 10.6 The Utility of Reliability: Optimum System Selection 299 10.6.1 Decision-making for System Selection 300 10.6.2 The Utility of Reliability 301 10.7 Multi-state and Vague Stochastic Systems 303 10.7.1 Vagueness or Imprecision 304 10.7.2 Many-valued Logic: A Synopsis 305 10.7.3 Consistency Profiles and Probabilities of Vague Sets 305 10.7.4 Reliability of Components in Vague Binary States 307 10.7.5 Reliability of Systems in Vague Binary States 307 10.7.6 Concluding Comments on Vague Stochastic Systems 308 11 Reliability and Survival in Econometrics and Finance 309 11.1 Introduction and Overview 309 11.2 Relating Metrics of Reliability to those of Income Inequality 310 11.2.1 Some Metrics of Reliability and Survival 310 11.2.2 Metrics of Income Inequality 311 11.2.3 Relating the Metrics 313 11.2.4 The Entropy of Income Shares 315 11.2.5 Lorenz Curve Analysis of Failure Data 315 11.3 Invoking Reliability Theory in Financial Risk Assessment 317 11.3.1 Asset Pricing of Risk-free Bonds: An Overview 317 11.3.2 Re-interpreting the Exponentiation Formula 319 11.3.3 A Characterization of Present Value Functions 320 11.3.4 Present Value Functions Under Stochastic Interest Rates 325 11.4 Inferential Issues in Asset Pricing 328 11.4.1 Formulating the Inferential Problem 329 11.4.2 A Strategy for Pooling Present Value Functions 329 11.4.3 Illustrative Example: Pooling Present Value Functions 331 11.5 Concluding Comments 332 Appendix A Markov Chain Monté Carlo Simulation 335 A.1 The Gibbs Sampling Algorithm 335 Appendix B Fourier Series Models and the Power Spectrum 339 B.1 Preliminaries: Trigonometric Functions 339 B.2 Orthogonality of Trigonometric Functions 340 B.3 The Fourier Representation of a Finite Sequence of Numbers 341 B.4 Fourier Series Models for Time Series Data 342 B.4.1 The Spectrum and the Periodgram of f(t) 343 Appendix C Network Survivability and Borel’s Paradox 345 C.1 Preamble 345 C.2 Re-assessing Testimonies of Experts Who have Vanished 345 C.3 The Paradox in Two Dimensions 346 C.4 The Paradox in Network Survivability Assessment 347 Bibliography 349 Index 365

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Book SynopsisThe statistical analysis of cost-effectiveness data is becoming increasingly important within health and medical research. Statistical Analysis of Cost-Effectiveness Data provides a practical book that synthesises the huge amount of research that has taken place in the area over the last two decades.Trade Review"…Willan and Briggs have written a thorough, balanced, and useful book. I look forward to reading an enlarged, revised second edition." (Journal of the American Statistical Association, June 2008) "Overall this is a useful book for this new discipline that helps considerably the reader to enter this topic and learn how to handle this type of problems." (Zentralblatt MATH 2008)Table of ContentsPreface. 1. Concepts. 1.1 Introduction. 1.2 Cost-effectiveness data and the parameters of interest. 1.3 The cost-effectiveness plane, the ICER and INB. 1.4 Outline. 2. Parameter Estimation for Non-censored Data. 2.1 Introduction. 2.2 Cost. 2.3 Effectiveness. 2.4 Summary. 3. Parameter Estimation for Censored Data. 3.1 Introduction. 3.2 Mean Cost. 3.3 Effectiveness. 3.4 Summary. 4. Cost-effectiveness Analysis. 4.1 Introduction. 4.2 Incremental cost-effectiveness ratio. 4.3 Incremental net benefit. 4.4 The cost-effectiveness acceptability curve. 4.5 Using bootstrap methods. 4.6 A Bayesian incremental net benefit approach. 4.7 Kinked thresholds. 4.8 Summary. 5. Cost-effectiveness Analysis: Examples. 5.1 Introduction. 5.2 The CADET-Hp trial. 5.3 Symptomatic hormone-resistant prostate cancer. 5.4 The Canadian implantable defibrillator study (CIDS). 5.5 The EVALUATE trial. 5.6 Bayesian approach applied to the UK PDS study. 5.7 Summary. 6. Power and Sample Size Determination. 6.1 Introduction. 6.2 Approaches based on the cost-effectiveness plane. 6.3 The classical approach based on net benefit. 6.4 Bayesian take on the classical approach. 6.5 The value of information approach. 6.6 Summary. 7. Covariate Adjustment and Sub-group Analysis. 7.1 Introduction. 7.2 Non-censored data. 7.3 Censored data. 7.4 Summary. 8. Multicenter and Multinational Trials. 8.1 Introduction. 8.2 Background to multinational cost-effectiveness. 8.3 Fixed effect approaches. 8.4 Random effects approaches. 8.5 Summary. 9. Modeling Cost-effectiveness. 9.1 Introduction. 9.2 A general framework for modeling cost-effectiveness results. 9.3 Case study: an economic appraisal of the goal study. 9.4 Summary. References. Author Index. Subject Index. Series List.

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Book SynopsisThere is an increasing need to rein in the cost of scientific study without sacrificing accuracy in statistical inference. Optimal design is the judicious allocation of resources to achieve the objectives of studies using minimal cost via careful statistical planning.Trade Review"The wide range of analytical concepts and applications focused upon here will attract statisticians interested in design theory and researchers in many of the fields in which it is used." (Journal of the American Statistical Association, March 2006)Table of ContentsList of Contributors. Editors’ Foreword. 1 Optimal Design in Educational Testing (Steven Buyske). 1.1 Introduction. 1.2 Test Design . 1.3 Sampling Design. 1.4 Future Directions. 2 Optimal On-line Calibration of Testlets (Douglas H. Jones and Mikhail S. Nediak). 2.1 Introduction. 2.2 Background. 2.3 Solution for Optimal Designs. 2.4 Simulation Results. 2.5 Discussion. 3 On the Empirical Relevance of Optimal Designs for the Measurement of Preferences (Heiko Großmann, Heinz Holling, Michaela Brocke, Ulrike Graßhoff and Rainer Schwabe). 3.1 Introduction. 3.2 Conjoint Analysis. 3.3 Paired Comparison Models in Conjoint Analysis. 3.4 Design Issues. 3.5 Experiments. 3.6 Discussion. 4 Designing Optimal Two-stage Epidemiological Studies (Marie Reilly and Agus Salim). 4.1 Introduction. 4.2 Illustrative Examples. 4.3 Meanscore. 4.4 Optimal Design and Meanscore. 4.5 Deriving Optimal Designs in Practice. 4.6 Summary. 4.7 Appendix 1 Brief Description of Software Used. 4.8 Appendix 2 The Optimal Sampling Package. 4.9 Appendix 3 Using the Optimal Package in R. 4.10 Appendix 4 Using the Optimal Package in S-Plus. 4.11 Appendix 5 Using the Optimal Package in STATA. 5 Response-Driven Designs in Drug Development (Valerii V. Fedorov and Sergei L. Leonov). 5.1 Introduction. 5.2 Motivating Example: Quantal Models for Dose Response. 5.3 Continuous Models. 5.4 Variance Depending on Unknown Parameters and Multi-response Models. 5.5 Optimal Designs with Cost Constraints 5.6 Adaptive Designs 5.7 Discussion 6 Design of Experiments for Microbiological Models (Holger Dette, Viatcheslav B. Melas and Nikolay Strigul). 6.1 Introduction. 6.2 Experimental Design for Nonlinear Models. 6.3 Applications of Optimal Experimental Design in Microbiology. 6.4 Bayesian Methods for Regression Models. 6.5 Conclusions. 7 Selected Issues in the Design of Studies of Interrater Agreement (Allan Donner and Mekibib Altaye). 7.1 Introduction. 7.2 The Choice between a Continuous or Dichotomous Variable. 7.3 The Choice between a Polychotomous or Dichotomous Outcome Variable. 7.4 Incorporation of Cost Considerations. 7.5 Final Comments. 8 Restricted Optimal Design in the Measurement of Cerebral Blood Flow Using the Kety–Schmidt Technique (J.N.S. Matthews and P.W. James). 8.1 Introduction. 8.2 The Kety–Schmidt Method. 8.3 The Statistical Model and Optimality Criteria. 8.4 Locally Optimal Designs. 8.5 Bayesian Designs and Prior Distributions. 8.6 Optimal Bayesian Designs. 8.7 Practical Designs. 8.8 Concluding Remarks. 9 Optimal Experimental Design for Parameter Estimation and Contaminant Plume Characterization in Groundwater Modelling (James McPhee and William W-G. Yeh). 9.1 Introduction. 9.2 Groundwater Flow and Mass Transport in Porous Media: Modelling Issues. 9.3 Problem Formulation. 9.4 Solution Algorithms. 9.5 Case Studies. 9.6 Summary and Conclusions. 10 The Optimal Design of Blocked Experiments in Industry (Peter Goos, Lieven Tack and Martina Vandebroek). 10.1 Introduction. 10.2 The Pastry Dough Mixing Experiment. 10.3 The Problem. 10.4 Fixed Block Effects Model. 10.5 Random Block Effects Model. 10.6 The Pastry Dough Mixing Experiment Revisited. 10.7 Time Trends and Cost Considerations. 10.8 Optimal Run Orders for Blocked Experiments. 10.9 A Time Trend in the Pastry Dough Mixing Experiment. 10.10 Summary. Index.

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Book SynopsisThe Encyclopedia of Statistics in Behavioral Science encompasses the refined statistical concepts and techniques that are essential to the advancement in psychology and its ever-widening fields of application.Trade Review"I congratulate the editors for a job well done. The coverage is thorough and accurate, and the levels are well matched to the anticipated readers." (Journal of the American Statistical Association) "... valuable for researcher[s], educators, and students in... neuroscience, behavior genetics, and the cognitive sciences... as well as for those in the fields of sociology, management sciences, market research, and human resources." (American Reference Books Annual, March 2007) "All libraries catering for post-16-year-olds should encourage their readers to work through these elementary texts." (Reference Reviews, 2006)Table of ContentsVOLUME 1 A Priori v Post Hoc Testing. ACE Model. Adaptive Random Assignment. Adaptive Sampling. Additive Constant Problem. Additive Genetic Variance. Additive Models. Additive Tree. Additivity Tests. Adoption Studies. Age-Period-Cohort Analysis. Akaike's Criterion. Allelic Association. All-X Models. All-Y Models. Alternating Treatment Designs. Analysis of Covariance. Analysis of Covariance: Nonparametric. Analysis of Variance. Analysis of Variance and Multiple Regression Approaches. Analysis of Variance: Cell Means Approach. Analysis of Variance: Classification. Ansari-Bradley Test. Arbuthnot, John. Area Sampling. Arithmetic Mean. Ascertainment Corrections. Assortative Mating. Asymptotic Relative Efficiency. Attitude Scaling. Attrition. Average Deviation. Axes in Multivariate Analysis. Bagging. Balanced Incomplete Block Designs. Bar Chart. Battery Reduction. Bayes, Thomas. Bayesian Belief Networks. Bayesian Item Response Theory Estimation. Bayesian Methods for Categorical Data. Bayesian Statistics. Bernoulli Family. Binomial Confidence Interval. Binomial Distribution: Estimating and Testing parameters. Binomial Effect Size Display. Binomial Test. Biplot. Block Random Assignment. Boosting. Bootstrap Inference. Box Plots. Bradley-Terry Model. Breslow-Day Statistic. Brown, William. Bubble Plot. Burt, Cyril Lodowic. Bush, Robert R. Calculating Covariance. Campbell, Donald T. Canonical Correlation Analysis. Carroll-Arabie Taxonomy. Carryover and Sequence Effects. Case Studies. Case-Cohort Studies. Case-Control Studies. Catalogue of Parametric Tests. Catalogue of Probability Density Functions. Catastrophe Theory. Categorizing Data. Cattell, Raymond Bernard. Censored Observations. Census. Centering in Multivariate Linear Models. Central Limit Theory. Children of Twins Design. Chi-Square Decomposition. Cholesky Decomposition. Classical Statistical Inference: Practice versus Presentation. Classical Statistical Inference Extended: Split-Tailed Tests. Classical Test Models. Classical Test Score Equating. Classification and Regression Trees. Clinical Psychology. Clinical Trials and Intervention Studies. Cluster Analysis: Overview. Clustered Data. Cochran, William Gemmell. Cochran's C Test. Coefficient of Variation. Cohen, Jacob. Cohort Sequential Design. Cohort Studies. Coincidences. Collinearity. Combinatorics for Categorical Variables. Common Pathway Model. Community Intervention Studies. Comorbidity. Compensatory Equalization. Compensatory Rivalry. Completely Randomized Design. Computational Models. Computer-Adaptive Testing. Computer-Based Test Designs. Computer-Based Testing. Concordance Rates. Conditional Independence. Conditional Standard Errors of Measurement. Confidence Intervals. Confidence Intervals: Nonparametric. Configural Frequency Analysis. Confounding in the Analysis of Variance. Confounding Variable. Contingency Tables. Coombs, Clyde Hamilton. Correlation. Correlation and Covariance Matrices. Correlation Issues in Genetics Research. Correlation Studies. Correspondence Analysis. Co-Twin Control Methods. Counter Null Value of an Effect Size. Counterbalancing. Counterfactual Reasoning. Covariance. Covariance Matrices: Testing Equality of. Covariance Structure Models. Covariance/variance/correlation. Cox, Gertrude Mary. Cramer-Von Mises Test. Criterion-Referenced Assessment. Critical Region. Cross Sectional Design. Cross-Classified and Multiple Membership Models. Cross-Lagged Panel Design. Crossover Design. Cross-validation. Cultural Transmission. Data Mining. de Finetti, Bruno. de Moivre, Abraham. Decision Making Strategies. Deductive Reasoning and Statistical Inference. DeFries-Fulker Analysis. Demand Characteristics. Deming, Edwards William. Design Effects. Development of Statistical Theory in the 20th Century. Differential Item Functioning. Direct and Indirect Effects. Direct Maximum Likelihood Estimation. Directed Alternatives in Testing. Direction of Causation Models. Discriminant Analysis. Distribution Free Inference, an Overview. Dominance. Dot chart. Dropouts in Longitudinal Data. Dropouts in Longitudinal Studies: Methods of Analysis. Dummy Variables. VOLUME 2 Ecological Fallacy. Educational Psychology: Measuring Change Over Time. Effect Size Measures. Eigenvalue/Eigenvector. Empirical Quantile-Quantile Plots. Epistasis. Equivalence Trials. Error Rates. Estimation. ETA and ETA Squared. Ethics in Research. Evaluation Research. Event History Analysis. Exact Methods for Categorical Data. Expectancy Effect. Expectation. Experimental Design. Exploratory Data Analysis. External Validity. Facet Theory. Face-to-Face Surveys. Factor Analysis of Personality Measures. Factor Analysis: Confirmatory. Factor Analysis: Exploratory. Factor Analysis: Multiple Groups. Factor Analysis: Multitrait-Multimethod. Factor Score Estimation. Factorial Designs. Family History Versus Family Study Methods in Genetics. Family Study and Relative Risk. Fechner, Gustav T. Field Experiment. Finite Mixture Distributions. Fisher, Sir Ronald Aylmer. Fisherian Tradition in Behavioral Genetics. Fixed and Random Effects. Fixed Effect Models. Focus Group Techniques. Free Response Data Scoring. Friedman's Test. Functional Data Analysis. Fuzzy Cluster Analysis. Galton, Francis. Game Theory. Gauss, Johann Carl Friedrich. Gene-Environment Correlation. Gene-Environment Interaction. Generalizability. Generalizability Theory: Basics. Generalizability Theory: Estimation. Generalizability Theory: Overview. Generalized Additive Model. Generalized Estimating Equations (GEE). Generalized Linear Mixed Models. Generalized Linear Models (GLM). Genotype. Geometric Mean. Goodness of Fit. Goodness of Fit for Categorical Variables. Gosset, William Sealy. Graphical Chain Models. Graphical Methods Pre-twentieth Century. Graphical Presentation of Longitudinal Data. Growth Curve Modeling. Guttman, Louise (Eliyahu). Harmonic Mean. Hawthorne Effect. Heritability. Heritability: Overview. Heteroscedasticity and Complex Variation. Heuristics. Heuristics: Fast and Frugal. Hierarchical Clustering. Hierarchical Item Response Theory Modeling. Hierarchical Models. High-Dimensional Regression. Hill's Criteria of Causation. Histogram. Historical Controls. History of Analysis of Variance. History of Behavioral Statistics. History of Correlational Measurement. History of Discrimination and Clustering. History of Factor Analysis: British Perspective. History of Factor Analysis: Statistical Perspective. History of Intelligence Measurement. History of Mathematical Learning Theory. History of Multivariate Analysis of Variance. History of Path Analysis. History of Psychometrics. History of Surveys of Sexual Behavior. History of the Control Group. Hodges-Lehman Estimator. Horseshoe Pattern. Hotelling, Howard. Hull, Clark L. Identification. Inbred Strain Study. Incidence. Incomplete Contingency Tables. Incompleteness of Probability Models. Independence: Chi-square and likelihood Ratio Tests. Independent Components Analysis. Independent Pathway Model. Index Plots. Industrial/Organizational Psychology. Influential Observations. Information Matrix. Information Theory. Instrumental Variable. Intention-to-Treat. Interaction Effects. Interaction Plot. Internal Consistency. Internal Validity. Internet Research Methods. Interquartile Range. Interrupted Time Series Design. Intervention Analysis. Intraclass Correlation. Intrinsic Linearity. Introduction to Probability. INUS Conditions. Item Analysis. Item Bias Detection: Classical Approaches. Item Bias Detection: Modern Approaches. Item Exposure Detection. Item Response Theory (IRT) Models for Dichotomous Data. Item Response Theory Models for Rating Scale Data. Item Response Theory: Cognitive Models. Item Response Theory Models for Polytomous Response Data. Jackknife. Jonckheere-Terpstra Test. Kendall, Maurice George. Kendall's Coefficient of Concordance. Kendall's Tau - t. Kernel Smoothing. K-Means Analysis. Kolmogorov, Andrey Nikolaevich. Kolmogorov-Smirnov Tests. Kruskal-Wallis Test. Kurtosis. Laplace, Pierre Simon (Marquis de). Latent Class Analysis. Latent Transition Models. Latent Variable. Latin Squares Designs. Laws of Large Numbers. Least Squares Estimation. Leverage Plot. Liability Threshold Models. Linear Model. Linear Models: Permutation Methods. Linear Multilevel Models. Linkage Analysis. Logistic Regression. Log-linear Models. Log-linear Rasch Models for Stability and Change. Longitudinal Data Analysis. Longitudinal Designs in Genetic Research. Lord, Frederic Mather. Lord's Paradox. VOLUME 3 M Estimators of Location. Mahalanobis Distance. Mahalanobis, Prasanta Chandra. Mail Surveys. Mallows Cp Statistic. Mantel-Haenszel Methods. Marginal Independence. Marginal Models for Clustered Data. Markov Chain Monte Carlo and Bayesian Statistics. Markov Chain Monte-Carlo Item Response Theory Estimation. Markov Chains. Markov, Andrei Andreevich.. Martingales. Matching. Mathematical Psychology. Maximum Likelihood Estimation. Maximum Likelihood Item Response Theory Estimation. Maxwell, Albert Ernest. Measurement: Overview. Measures of Association. Median. Median Absolute Deviation. Median Test. Mediation. Mendelian Genetics Rediscovered. Mendelian Inheritance and Segregation Analysis. Meta-Analysis. Microarrays. Mid-p Values. Minimum Spanning Tree. Misclassification Rates. Missing Data. Model Based Cluster Analysis. Model Evaluation. Model Fit: Assessment of. Model Identifiability. Model Selection. Models for Matched Pairs. Moderation. Moments. Monotonic Regression. Monte Carlo Goodness of Fit Tests. Monte Carlo Simulation. Multidimensional Item Response Theory Models. Multidimensional Scaling. Multidimensional Unfolding. Multigraph Modeling. Multilevel and SEM Approaches to Growth Curve Modeling. Multiple Baseline Designs. Multiple Comparison Procedures. Multiple Comparison Tests: Nonparametric and Resampling Approaches. Multiple Imputation. Multiple Informants. Multiple Linear Regression. Multiple Testing. Multi-trait Multi-method Analyses. Multivariate Analysis of Variance. Multivariate Analysis: Bayesian. Multivariate Analysis: Overview. Multivariate Genetic Analysis. Multivariate Multiple Regression. Multivariate Normality Tests. Multivariate Outliers. Neural Networks. Neuropsychology. New Item Types and Scoring. Neyman, Jerzy. Neyman-Pearson Inference. Nightingale, Florence. Nonequivalent Control Group Design. Nonlinear Mixed Effects Models. Nonlinear Models. Nonparametric Correlation (rs). Nonparametric Correlation (tau). Nonparametric Item Response Theory Models. Nonparametric Regression. Nonrandom Samples. Nonresponse in Sample Surveys. Nonshared Environment. Normal Scores & Expected Order Statistics. Nuisance Variables. Number Needed to Treat. Number of Clusters. Number of Matches and Magnitude of Correlation. Observational Study. Odds and Odds Ratios. One Way Designs:Nonparametric and Resampling Approaches. Optimal Design for Categorical Variables. Optimal Scaling. Optimization Methods. Ordinal Regression Models. Outlier Detection. Outliers. Overlapping Clusters. P Values. Page's 0rdered Alternatives Test. Paired Observations, Distribution Free Methods. Panel Study. Paradoxes. Parsimony/Occham's Razor. Partial Correlation Coefficients. Partial Least Squares. Path Analysis and Path Diagrams. Pattern Recognition. Pearson Product Moment Correlation. Pearson, Egon Sharpe. Pearson, Karl. Percentiles. Permutation Based Inference. Person Misfit. Pie Chart. Pitman Test. Placebo Effect. Point Biserial Correlation. Polychoric Correlation. Polynomial Model. Population Stratification. Power. Power Analysis for Categorical Methods. Power and Sample Size in Multilevel Linear Models. Prediction Analysis of Cross-Classifications. Prevalence. Principal Component Analysis. Principal Components and Extensions. Probability Plots. Probability: Foundations of. Probits. Procrustes Analysis. Projection Pursuit. Propensity Score. Proscriptive and Retrospective Studies. Proximity Measures. Psychophysical Scaling. Qualitative Research. Quantiles. Quantitative Methods in Personality Research. Quartiles. Quasi-Experimental Designs. Quasi-Independence. Quasi-Symmetry in Contingency Tables. Quetelet, Adolphe. VOLUME 4 R & Q Analysis. R Squared, Adjusted R Squared. Random Effects and Fixed Effects Fallacy. Random Effects in Multivariate Linear Models: Prediction. Random Forests. Random Walks. Randomization. Randomization Based Tests. Randomized Block Design: Nonparametric Analyses. Randomized Block Designs. Randomized Response Technique. Range. Rank Based Inference. Rasch Modeling. Rasch Models for Ordered Response Categories. Rater Agreement. Rater Agreement - Kappa. Rater Agreement - Weighted Kappa. Rater Bias Models. Reactivity. Receiver Operating Characteristics Curves. Recursive Models. Regression Artifacts. Regression Discontinuity Design. Regression Model Coding for the Analysis of Variance. Regression Models. Regression to the Mean. Relative Risk. Reliability: Definitions and Estimation. Repeated Measures Analysis of Variance. Replicability of Results. Reproduced Matrix. Resentful Demoralization. Residual Plot. Residuals. Residuals in Structural Equation, Factor Analysis, and Path Analysis Models. Resistant Line Fit. Retrospective Studies. Reversal Design. Risk Perception. Robust Statistics for Multivariate Methods. Robust Testing Procedures. Robustness of Standard Tests. Runs Test. Sample Size and Power Calculation. Sampling Distributions. Sampling Issues in Categorical Data. Saturated Model. Savage, Leonard Jimmie. Scales of Measurement. Scaling Asymmetric Matrices. Scaling of Preferential Choice. Scatterplot Matrices. Scatterplot Smoothers. Scatterplots. Scheffe, Henry. Second order Factor Analysis: Confirmatory. Selection Study (Mouse Genetics). Sensitivity Analysis. Sensitivity Analysis in Observational Studies. Sequential Decision Making. Sequential Testing. Setting Performance Standards - Issues, Methods. Sex-Limitation Models. Shannon, Claude. Shared Environment. Shepard Diagram. Sibling Interaction Effects. Sign Test. Signal Detection Theory. Signed Ranks Test. Simple Random Assignment. Simple Random Sampling. Simple V Composite Tests. Simulation Methods for Categorical Variables. Simultaneous Confidence Interval. Single and Double-Blind Procedures. Single-Case Designs. Skewness. Slicing Inverse Regression. Snedecor, George Waddell. Social Interaction Models. Social Networks. Social Psychology. Social Validity. Software for Behavioral Genetics. Software for Statistical Analyses. Spearman, Charles Edward. Spearman's Rho. Sphericity Test. Standard Deviation. Standard Error. Standardized Regression Coefficients. Stanine Scores. Star and Profile Plots. State Dependence. Statistical Models. Statistical Models for Causation: A Critical Review. Stem and Leaf Plot. Stephenson, William. Stevens, S S. Stratification. Structural Equation Modeling and Test Validation. Structural Equation Modeling: Categorical Variables. Structural Equation Modeling: Checking Substantive Plausibility. Structural Equation Modeling: Mixture Models. Structural Equation Modeling: Multilevel. Structural Equation Modeling: Nontraditional Alternatives. Structural Equation Modeling: Overview. Structural Equation Modeling: Software. Structural Equation Modeling:Latent Growth Curve Analysis. Structural Equation Modeling:Nonstandard Cases. Structural Zeros. Subjective Probability and Human Judgement. Summary Measure Analysis of Longitudinal Data. Survey Questionnaire Design. Survey Sampling Procedures. Survival Analysis. Symmetry Plot. Symmetry: Distribution Free Tests for. Tau-Equivalent and Congeneric Measurements. Teaching Statistics to Psychologists. Teaching Statistics: Sources. Telephone Surveys. Test Bias Detection. Test Construction. Test Construction: Automated. Test Dimensionality: Assessment of. Test Translation. Tetrachoric Correlation. Theil Slope Estimate. Thomson, Godfrey Hilton. Three dimensional (3D) Scatterplots. Three-Mode Component and Scaling Methods. Thurstone, Louis Leon. Time Series Analysis. Tolerance and Variance Inflation Factor. Transformation. Tree Models. Trellis Graphics. Trend Tests for Counts and Proportions. Trimmed Means. T-Scores. Tukey Quick Test. Tukey, John Wilder. Tversky, Amos. Twin Designs. Twins Reared Apart Design. Two by Two Contingency Tables. Two-mode Clustering. Two-way Factorial: Distribution-Free Methods. Type I, Type II and Type III Sums of Squares. Ultrametric Inequality. Ultrametric Trees. Unidimensional Scaling. Urban, F M. Utility Theory. Validity Theory and Applications. Variable Selection. Variance. Variance Components. Walsh Averages. Wiener, Norbert. Wilcoxon, Frank. Wilcoxon-Mann-Whitney test. Winsorized Robust Measures. Within Case Designs: Distribution Free Methods. Yates' Correction. Yates, Frank. Yule, George Udny. Z-Scores.

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Book SynopsisDose-finding experiments define the safe dosage of a drug in development, in terms of the quantity given to a patient. Statistical methods play a crucial role in identifying optimal dosage. Used appropriately, these methods provide reliable results and reduce trial duration and costs. In practice, however, dose-finding is often done poorly, with widely used conventional methods frequently being unreliable, leading to inaccurate results. However, there have been many advances in recent years, with new statistical techniques being developed and it is important that these new techniques are utilized correctly. Statistical Methods for Dose-Finding Experiments reviews the main statistical approaches for dose-finding in phase I/II clinical trials and presents practical guidance on their correct use. Includes an introductory section, summarizing the essential concepts in dose-finding. Contains a section on algorithm-based approaches, such as the traditioTrade Review"This book is an important collaboration of leading experts in the area. Primarily aimed at statisticians and clinicians working in clinical trials and medical research, there is also much to benefit graduate students of biostatistics." (Zentralblatt MATH, 2011) "It represents a good investment and would be a useful addition to the pharmaceutical researcher's library." (Technometrics, August 2008) "This book will play an important role in establishing some of the recent innovative dose-finding methods in this fast evolving field." (Journal of Biopharmaceutical Statistics, September 2007) "…very valuable to anyone working in the field...a very useful and complete textbook for graduate education." (AIChE Journal, October 2007) "…an easily readable comprehensive compilation of several decades of work in dose finding designs and issues…" (Journal of the American Statistical Association, September 2007) Table of ContentsContributors. Preface. Introduction (Sylvie Chevret). Part I. General Principles and Controversial Issues in Dose-Findings. 1. Basic concepts in dose-finding (Sylvie Chevret). 1.1 Main concepts. 1.2 Main issues from a pharmaceutical point of view. 1.3 Statistical issues of dose-finding phase I trials. 1.4 Conclusion. References. 2. Philosophy and methodology of dose-finding – a regulatory perspective (Robert Hemmings). 2.1 Introduction. 2.2 In search of the optimal dose. 2.3 Regulatory requirements for drug licensure. 2.4 Benefits to the sponsor and the patient of providing data on dose-response over and above those required for licensing. 2.5 Trial designs for determining dose-response. 2.6 Discussion. References. Part II. Algorithm-Based Approaches. 3. Traditional and modified algorithm-based designs for phase I cancer clinical trials (Weichung Joe Shih and Yong Lin). 3.1 Introduction. 3.2 Notation and convention. 3.3 Traditional algorithm-based designs. 3.4 Modified algorithm-based designs. 3.5 Probability of a dose being chosen as the MTD. 3.6 Expected number of patients treated at each dose level. 3.7 Other statistical properties. 3.8 Examples. 3.9 Discussion. Acknowledgments. References. 4. Accelerated titration designs (Janet Dancey, Boris Freidlin and Larry Rubinstein) 4.1 Introduction. 4.2 Design. 4.3 Evaluation of performance. 4.4 Model-based analysis. 4.5 Clinical applications. 4.6 Conclusions. References. 5. Group up-and-down designs in toxicity studies (Anna Ivanova and Nancy Flournoy). 5.1 Introduction. 5.2 Group up-and-down designs for phase I clinical trials. 5.3 Designs for acute toxicity studies. 5.4 Fully sequential designs for phase I clinical trials. 5.5 Start-up rules. 5.6 Estimation. 5.7 Up-and-down designs to find the dose with maximum success probability. 5.8 Discussion. References. Part III. Model-Based Approaches. 6. The continual reassessment method (Sylvie Chevret and Sarah Zohar). 6.1 Introduction. 6.2 The original continual reassessment method (CRM). 6.3 Likelihood CRM (CRML). 6.4 Modified continual reassessment method (MCRM). 6.5 Concluding remarks. References. 7. Using Bayesian decision theory in dose-escalation studies (John Whitehead). 7.1 Introduction. 7.2 Example of a dose-escalation study. 7.3 A statistical model for the study. 7.4 Prior information. 7.5 Gain functions. 7.6 Safety constraints and stopping rules. 7.7 Evaluation of the Bayesian approach. 7.8 Discussion. References. 8. Dose-escalation with overdose control (Mourad Tighiouart and Andr´e Rogatko). 8.1 Introduction. 8.2 Escalation with overdose control design. 8.3 Adjusting for covariates. 8.4 Choice of prior distributions. 8.5 Concluding remarks. References. 9. Dose-escalation methods for phase I healthy volunteer studies (Y. Zhou). 9.1 Introduction. 9.2 Frequentist analysis. 9.3 A Bayesian analysis. 9.4 Conducting dose-escalation using a Bayesian decision–theoretic approach. 9.5 Multiple simulations. 9.6 Conclusions. Acknowledgements. References. Part IV. Future Trends for Past Issues. 10. Defining stopping rules (Sarah Zohar). 10.1 Introduction. 10.2 Background. 10.3 Dose-finding specificities. 10.4 Examples. 10.5 Conclusions. Acknowledgment. References. 11. Dose-finding with delayed binary outcomes in cancer trials (Ying Kuen Cheung). 11.1 Introduction. 11.2 Review of current practice. 11.3 Basic methods. 11.4 An example. 11.5 Simulation results. 11.6 Chronic toxicities. 11.7 Discussion. 11.8 Bibliographic notes. References. 12. Dose-finding based on multiple ordinal toxicities in phase I oncology trials (B. Nebiyou Bekele and Peter F. Thall). 12.1 Introduction. 12.2 Probability model. 12.3 Dose-finding algorithm. 12.4 Elicitation process. 12.5 Application to the sarcoma trial. 12.6 Simulation study and sensitivity analyses. 12.7 Concluding remarks. References. 13. A two-stage design for dose-finding with two agents (Peter F. Thall) 13.1 Introduction. 13.2 Dose-toxicity model. 13.3 A two-stage dose-finding algorithm. 13.4 Application. 13.5 Discussion. References. 14. Using both efficacy and toxicity for dose-finding (Peter F. Thall and John D. Cook). 14.1 Introduction. 14.2 Illustrative trial. 14.3 Dose-outcome models. 14.4 The dose-finding algorithm. 14.5 Simulation studies. 14.6 Discussion. References. Part V. Conclusions. 15. Websites and software (Sarah Zohar). 15.1 Introduction. 15.2 Computation methods using statistical software. 15.3 Phase I or phase II dose-finding software. 15.4 Phase I/II dose-finding software. 15.5 Conclusions. References. Appendix: random numbers generation. Index.

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Book SynopsisMathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty.Trade Review"…an invaluable reference for researchers, practitioners and graduate students in several areas of statistics, engineering and technology." (Mathematical Reviews, 2007b) "...whether one is a researcher, student, or an industrial practitioner dealing with computer models the book comes highly recommended." (Technometrics, February 2007)Table of ContentsPreface ix 1 Introduction 1 1.1 Wags and Bogsats 1 1.2 Uncertainty analysis and decision support: a recent example 4 1.3 Outline of the book 9 2 Assessing Uncertainty on Model Input 13 2.1 Introduction 13 2.2 Structured expert judgment in outline 14 2.3 Assessing distributions of continuous univariate uncertain quantities 15 2.4 Assessing dependencies 16 2.5 Unicorn 20 2.6 Unicorn projects 20 3 Bivariate Dependence 25 3.1 Introduction 25 3.2 Measures of dependence 26 3.2.1 Product moment correlation 26 3.2.2 Rank correlation 30 3.2.3 Kendall’s tau 32 3.3 Partial, conditional and multiple correlations 32 3.4 Copulae 34 3.4.1 Fréchet copula 36 3.4.2 Diagonal band copula 37 3.4.3 Generalized diagonal band copula 41 3.4.4 Elliptical copula 42 3.4.5 Archimedean copulae 45 3.4.6 Minimum information copula 47 3.4.7 Comparison of copulae 49 3.5 Bivariate normal distribution 50 3.5.1 Basic properties 50 3.6 Multivariate extensions 51 3.6.1 Multivariate dependence measures 51 3.6.2 Multivariate copulae 53 3.6.3 Multivariate normal distribution 53 3.7 Conclusions 54 3.8 Unicorn projects 55 3.9 Exercises 61 3.10 Supplement 67 4 High-dimensional Dependence Modelling 81 4.1 Introduction 81 4.2 Joint normal transform 82 4.3 Dependence trees 86 4.3.1 Trees 86 4.3.2 Dependence trees with copulae 86 4.3.3 Example: Investment 90 4.4 Dependence vines 92 4.4.1 Vines 92 4.4.2 Bivariate- and copula-vine specifications 96 4.4.3 Example: Investment continued 98 4.4.4 Partial correlation vines 99 4.4.5 Normal vines 101 4.4.6 Relationship between conditional rank and partial correlations on a regular vine 101 4.5 Vines and positive definiteness 105 4.5.1 Checking positive definiteness 105 4.5.2 Repairing violations of positive definiteness 107 4.5.3 The completion problem 109 4.6 Conclusions 111 4.7 Unicorn projects 111 4.8 Exercises 115 4.9 Supplement 116 4.9.1 Proofs 116 4.9.2 Results for Section 4.4.6 127 4.9.3 Example of fourvariate correlation matrices 129 4.9.4 Results for Section 4.5.2 130 5 Other Graphical Models 131 5.1 Introduction 131 5.2 Bayesian belief nets 131 5.2.1 Discrete bbn’s 132 5.2.2 Continuous bbn’s 133 5.3 Independence graphs 141 5.4 Model inference 142 5.4.1 Inference for bbn’s 143 5.4.2 Inference for independence graphs 144 5.4.3 Inference for vines 145 5.5 Conclusions 150 5.6 Unicorn projects 150 5.7 Supplement 157 6 Sampling Methods 159 6.1 Introduction 159 6.2 (Pseudo-) random sampling 160 6.3 Reduced variance sampling 161 6.3.1 Quasi-random sampling 161 6.3.2 Stratified sampling 164 6.3.3 Latin hypercube sampling 166 6.4 Sampling trees, vines and continuous bbn’s 168 6.4.1 Sampling a tree 168 6.4.2 Sampling a regular vine 169 6.4.3 Density approach to sampling regular vine 174 6.4.4 Sampling a continuous bbn 174 6.5 Conclusions 180 6.6 Unicorn projects 180 6.7 Exercise 184 7 Visualization 185 7.1 Introduction 185 7.2 A simple problem 186 7.3 Tornado graphs 186 7.4 Radar graphs 187 7.5 Scatter plots, matrix and overlay scatter plots 188 7.6 Cobweb plots 191 7.7 Cobweb plots local sensitivity: dike ring reliability 195 7.8 Radar plots for importance; internal dosimetry 199 7.9 Conclusions 201 7.10 Unicorn projects 201 7.11 Exercises 203 8 Probabilistic Sensitivity Measures 205 8.1 Introduction 205 8.2 Screening techniques 205 8.2.1 Morris’ method 205 8.2.2 Design of experiments 208 8.3 Global sensitivity measures 214 8.3.1 Correlation ratio 215 8.3.2 Sobol indices 219 8.4 Local sensitivity measures 222 8.4.1 First order reliability method 222 8.4.2 Local probabilistic sensitivity measure 223 8.4.3 Computing ∂E(X|g o) ∂ go 225 8.5 Conclusions 227 8.6 Unicorn projects 228 8.7 Exercises 230 8.8 Supplement 236 8.8.1 Proofs 236 9 Probabilistic Inversion 239 9.1 Introduction 239 9.2 Existing algorithms for probabilistic inversion 240 9.2.1 Conditional sampling 240 9.2.2 Parfum 242 9.2.3 Hora-Young and PREJUDICE algorithms 243 9.3 Iterative algorithms 243 9.3.1 Iterative proportional fitting 244 9.3.2 Iterative PARFUM 245 9.4 Sample re-weighting 246 9.4.1 Notation 246 9.4.2 Optimization approaches 247 9.4.3 IPF and PARFUM for sample re-weighting probabilistic inversion 248 9.5 Applications 249 9.5.1 Dispersion coefficients 249 9.5.2 Chicken processing line 252 9.6 Convolution constraints with prescribed margins 253 9.7 Conclusions 255 9.8 Unicorn projects 256 9.9 Supplement 258 9.9.1 Proofs 258 9.9.2 IPF and PARFUM 263 10 Uncertainty and the UN Compensation Commission 269 10.1 Introduction 269 10.2 Claims based on uncertainty 270 10.3 Who pays for uncertainty 272 Bibliography 273 Index 281

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Book SynopsisThe term Financial Derivative is a very broad term which has come to mean any financial transaction whose value depends on the underlying value of the asset concerned. Sophisticated statistical modelling of derivatives enables practitioners in the banking industry to reduce financial risk and ultimately increase profits made from these transactions. The book originally published in March 2000 to widespread acclaim.Thisrevised edition has been updated with minor corrections and new references, and now includes a chapter of exercises and solutions, enabling use as a course text. Comprehensive introduction to the theory and practice of financial derivatives. Discusses and elaborates on the theory of interest rate derivatives, an area of increasing interest. Divided into two self-contained parts ? the first concentrating on the theory of stochastic calculus, and the second describes in detail the pricing of a number of different derivatives in practice.Table of ContentsPreface to revised edition. Preface. Acknowledgements. Part I: Theory. 1 Single-Period Option Pricing. 1.1 Option pricing in a nutshell. 1.2 The simplest setting. 1.3 General one-period economy. 1.4 A two-period example. 2 Brownian Motion. 2.1 Introduction. 2.2 Definition and existence. 2.3 Basic properties of Brownian motion. 2.4 Strong Markov property. 3 Martingales. 3.1 Definition and basic properties. 3.2 Classes of martingales. 3.3 Stopping times and the optional sampling theorem. 3.4 Variation, quadratic variation and integration. 3.5 Local martingales and semimartingales. 3.6 Supermartingales and the Doob—Meyer decomposition. 4 Stochastic Integration. 4.1 Outline. 4.2 Predictable processes. 4.3 Stochastic integrals: the L2 theory. 4.4 Properties of the stochastic integral. 4.5 Extensions via localization. 4.6 Stochastic calculus: Itô’s formula. 5 Girsanov and Martingale Representation. 5.1 Equivalent probability measures and the Radon—Nikodým derivative. 5.1.1 Basic results and properties. 5.2 Girsanov’s theorem. 5.3 Martingale representation theorem. 6 Stochastic Differential Equations. 6.1 Introduction. 6.2 Formal definition of an SDE. 6.3 An aside on the canonical set-up. 6.4 Weak and strong solutions. 6.5 Establishing existence and uniqueness: Itô theory. 6.6 Strong Markov property. 6.7 Martingale representation revisited. 7 Option Pricing in Continuous Time. 7.1 Asset price processes and trading strategies. 7.2 Pricing European options. 7.3 Continuous time theory. 7.4 Extensions. 8 Dynamic Term Structure Models. 8.1 Introduction. 8.2 An economy of pure discount bonds. 8.3 Modelling the term structure. Part II: Practice. 9 Modelling in Practice. 9.1 Introduction. 9.2 The real world is not a martingale measure. 9.3 Product-based modelling. 9.4 Local versus global calibration. 10 Basic Instruments and Terminology. 10.1 Introduction. 10.2 Deposits. 10.3 Forward rate agreements. 10.4 Interest rate swaps. 10.5 Zero coupon bonds. 10.6 Discount factors and valuation. 11 Pricing Standard Market Derivatives. 11.1 Introduction. 11.2 Forward rate agreements and swaps. 11.3 Caps and floors. 11.4 Vanilla swaptions. 11.5 Digital options. 12 Futures Contracts. 12.1 Introduction. 12.2 Futures contract definition. 12.3 Characterizing the futures price process. 12.4 Recovering the futures price process. 12.5 Relationship between forwards and futures. Orientation: Pricing Exotic European Derivatives. 13 Terminal Swap-Rate Models. 13.1 Introduction. 13.2 Terminal time modelling. 13.3 Example terminal swap-rate models. 13.4 Arbitrage-free property of terminal swap-rate models. 13.5 Zero coupon swaptions. 14 Convexity Corrections. 14.1 Introduction. 14.2 Valuation of ‘convexity-related’ products. 14.3 Examples and extensions. 15 Implied Interest Rate Pricing Models. 15.1 Introduction. 15.2 Implying the functional form DTS. 15.3 Numerical implementation. 15.4 Irregular swaptions. 15.5 Numerical comparison of exponential and implied swap-rate models. 16 Multi-Currency Terminal Swap-Rate Models. 16.1 Introduction. 16.2 Model construction. 16.3 Examples. 16.3.1 Spread options. Orientation: Pricing Exotic American and Path-Dependent Derivatives. 17 Short-Rate Models. 17.1 Introduction. 17.2 Well-known short-rate models. 17.3 Parameter fitting within the Vasicek—Hull—White model. 17.4 Bermudan swaptions via Vasicek—Hull—White. 18 Market Models. 18.1 Introduction. 18.2 LIBOR market models. 18.3 Regular swap-market models. 18.4 Reverse swap-market models. 19 Markov-Functional Modelling. 19.1 Introduction. 19.2 Markov-functional models. 19.3 Fitting a one-dimensional Markov-functional model to swaption prices. 19.4 Example models. 19.5 Multidimensional Markov-functional models. 19.5.1 Log-normally driven Markov-functional models. 19.6 Relationship to market models. 19.7 Mean reversion, forward volatilities and correlation. 19.7.1 Mean reversion and correlation. 19.7.2 Mean reversion and forward volatilities. 19.7.3 Mean reversion within the Markov-functional LIBOR model. 19.8 Some numerical results. 20 Exercises and Solutions. Appendix 1: The Usual Conditions. Appendix 2: L2 Spaces. Appendix 3: Gaussian Calculations. References. Index.

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Book SynopsisThe term Financial Derivative is a very broad term which has come to mean any financial transaction whose value depends on the underlying value of the asset concerned. This work features a comprehensive introduction to the theory and practice of financial derivatives. It also discusses and elaborates on the theory of interest rate derivatives.Table of ContentsPreface to revised edition xv Preface xvii Acknowledgements xxi Part I: Theory 1 1Single-Period option pricing 3 2 Brownian Motion 19 3 Martingales 3.5 Local martingales and semimartingales 56 3.5.1 The space cMloc56 3.5.2 Semimartingales 59 3.6 Supermartingales and the Doob—Meyer decomposition 61 4 StochasticIntegration63 4.1 Outline 63 4.2 Predictable processes 65 4.3 Stochastic integrals: the L2 theory 67 4.3.1 The simplest integral 68 4.3.2 The Hilbert space L2 (M) 69 4.3.3 The L2 integral 70 4.3.4 Modes of convergence to H • M 72 4.4 Properties of the stochastic integral 74 4.5 Extensionsvialocalization77 4.5.1 Continuous local martingales as integrators 77 4.5.2 Semimartingales as integrators 78 4.5.3 The end of the road! 80 4.6 Stochastic calculus: Itô’s formula 81 4.6.1 Integration by parts and Itô’s formula 81 4.6.2 Differential notation 83 4.6.3 Multidimensional version of Itô’s formula 85 4.6.4 Lévy’stheorem88 5 GirsanovandMartingaleRepresentation91 5.1 Equivalent probability measures and the Radon—Nikodým derivative 91 5.1.1 Basic results and properties 91 5.1.2 Equivalent and locally equivalent measures on a filtered space 95 5.1.3 Novikov’s condition 97 5.2 Girsanov’s theorem 99 5.2.1 Girsanov’s theorem for continuous semimartingales 99 5.2.2 Girsanov’s theorem for Brownian motion 101 5.3 Martingale representation theorem 105 5.3.1 The space I2 (M) and its orthogonal complement 106 5.3.2 Martingale measures and the martingale representation theorem 110 5.3.3 Extensions and the Brownian case 111 6 Stochastic Differential Equations 115 6.1 Introduction 115 6.2 Formal definition of an SDE 116 6.3 An aside on the canonical set-up 117 6.4 Weak and strong solutions 119 6.4.2 Strong solutions 121 6.4.3 Tying together strong and weak 124 6.5 Establishing existence and uniqueness:Itô theory 125 6.5.1 Picard—Lindelöf iteration and ODEs 126 6.5.2 A technical lemma 127 6.5.3 Existence and uniqueness for Lipschitz coefficients 130 6.6 Strong Markov property 134 6.7 Martingale representation revisited 139 7 Option Pricing in Continuous Time 141 7.1 Asset price processes and trading strategies 142 7.1.1 A model for asset prices 142 7.1.2 Self-financing trading strategies 144 7.2 Pricing European options 146 7.2.1 Option value as a solution to a PDE 147 7.2.2 Option pricing via an equivalent martingale measure 149 7.3 Continuous time theory 151 7.3.1 Information within the economy 152 7.3.2 Units, numeraires and martingale measures 153 7.3.3 Arbitrage and admissible strategies 158 7.3.4 Derivative pricing in an arbitrage-free economy 163 7.3.5 Completeness 164 7.3.6 Pricing kernels 173 7.4 Extensions 176 7.4.1 General payout schedules 176 7.4.2 Controlled derivative payouts 178 7.4.3 More general asset price processes 179 7.4.4 Infinite trading horizon 180 8 Dynamic Term Structure Models 183 8.1 Introduction 183 8.2 An economy of pure discount bonds 183 8.3 Modelling the term structure 187 8.3.1 Pure discount bond models 191 8.3.2 Pricing kernel approach 191 8.3.3 Numeraire models 192 8.3.4 Finite variation kernel models 194 8.3.5 Absolutely continuous (FVK) models 197 8.3.6 Short-rate models 197 8.3.7 Heath—Jarrow—Morton models 200 8.3.8 Flesaker—Hughston models 206 References 423 Index 427

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Book SynopsisSelection bias can, and does, occur, even in randomized clinical trials. Steps need to be taken in order to ensure that this does not compromise the integrity of clinical trials; hence "Selection Bias and Covariate Imbalances in Randomized Clinical Trials" offers a comprehensive treatment of the subject and the methodology involved.Trade Review"…the author presents a provocative discussion of randomization and TORSB." (Computational Statistics, January 2008) "…recommended to all practitioners and researchers in the field of clinical trials…" (Biometrics, December 2005) " … informative and interesting to read" (Phamaceutical Statistics Volume 5:2)Table of ContentsPreface. Part I: Is There a Problem with Reliability in Medical Studies? 1 An Evolution of Comparative Methodology. 1.1 Single-subject studies. 1.2 Case series and cohort studies. 1.3 Historical controls. 1.4 Parallel control groups. 1.5 Matched studies. 1.6 Randomization. 1.7 Advance randomization. 1.8 Allocation concealment. 1.9 Residual selection bias. 2 Susceptibility of Randomized Trials to Subversion and Selection Bias. 2.1 Can randomized trials be subverted? 2.2 If randomized trials are subverted, do they cease to be randomized trials? 2.3 What is masking? 2.4 What is allocation concealment? 2.5 A double standard. 2.6 What if allocation concealment could be ensured? 3 Evidence of Selection Bias in Randomized Trials. 3.1 The burden of proof regarding the existence of selection bias in randomized trials. 3.2 Indirect population-level evidence that selection bias exists in randomized trials. 3.3 Direct trial-level evidence that selection bias exists in randomized trials. 3.3.1 Heparin for myocardial infarction. 3.3.2 University Group Diabetes Program. 3.3.3 Talc and mustine for pleural effusions. 3.3.4 Tonsillectomy for recurrent throat infection in children. 3.3.5 Oxytocin and amniotomy for induction of labor. 3.3.6 Western Washington Intracoronary Streptokinase Trial. 3.3.7 RSV immune globulin in infants and young children with respiratory syncytial virus. 3.3.8 A trial to assess episiotomy. 3.3.9 Canadian National Breast Cancer Screening Study. 3.3.10 Surgical trial. 3.3.11 Lifestyle Heart Trial. 3.3.12 Coronary Artery Surgery Study. 3.3.13 Etanercept for children with juvenile rheumatoid arthritis. 3.3.14 Edinburgh Randomized Trial of Breast-Cancer Screening. 3.3.15 Captopril Prevention Project. 3.3.16 G¨oteborg (Swedish) Mammography Trial. 3.3.17 HIP Mammography Trial. 3.3.18 Hypertension Detection and Follow-Up Program. 3.3.19 Randomized trial to prevent vertical transmission of HIV-1. 3.3.20 Effectiveness trial of a diagnostic test. 3.3.21 South African trial of high-dose chemotherapy for metastatic breast cancer. 3.3.22 Randomized study of a culturally sensitive AIDS education program. 3.3.23 Runaway Youth Study. 3.3.24 Cluster randomized trial of palliative care. 3.3.25 Randomized trial of methadone with or without heroin. 3.3.26 Randomized NINDS trial of tissue plasminogen activator for acute ischemic stroke. 3.3.27 Norwegian Timolol Trial. 3.3.28 Laparoscopic versus open appendectomy. 3.3.29 The Losartan Intervention for Endpoint Reduction in Hypertension (LIFE) Study. 3.3.30 The Heart Outcomes Prevention Evaluation (HOPE) Study. 3.4 In search of better evidence. 4 Impact of Selection Bias in Randomized Trials. 4.1 Quantifying the prediction of future allocations: balanced blocks. 4.2 Quantifying prediction of future allocations: unbalanced blocks. 4.3 Quantifying covariate imbalance resulting from selection bias. 4.4 Quantifying the bias resulting from covariate imbalance. Part II: Actions to be Taken to Improve the Reliability of Medical Studies. 5 Preventing Selection Bias in Randomized Trials. 5.1 Minimizing the Impact of Selection Bias. 5.2 Biased Selection of Investigators. 5.3 Minimizing the prediction of future allocations. 5.3.1 The trade-off between selection bias and chronological bias. 5.3.2 Notation. 5.3.3 Varying the block sizes. 5.3.4 The maximal procedure. 5.3.5 Extensions. 6 Detecting Selection Bias in Randomized Trials. 6.1 Baseline Imbalances in Observed Covariates. 6.2 Testing for selection bias without baseline analyses. 6.3 The selection covariate. 6.4 The role of the reverse propensity score in third-order residual selection bias. 6.5 Using the reverse propensity score To Test for selection bias: the Berger–Exner test and graph. 6.6 Using the screening log to test for selection bias. 6.7 The Ivanova-Barrier-Berger (IBB) Detection Method. 6.8 Interpreting negative tests of selection bias. 6.9 When should one test for selection bias? 6.10 Who should test for selection bias? 7 Adjusting for Selection Bias in Randomized Trials. 7.1 Methods proposed for addressing non-random baseline imbalances. 7.2 Selection bias arising from a complete lack of allocation concealment. 7.3 Selection bias arising from imperfect allocation concealment. 7.3.1 The RPS approach to adjusting for selection bias. 7.3.2 The Ivanova–Barrier–Berger (IBB) method for correcting selection bias. 8 Managing Selection Bias in Randomized Trials. 8.1 Action points during the design phase of the trial. 8.2 Action points during the conduct of the trial. 8.3 Action points during the analysis of trial data. 8.4 Action points by party. References. Author Index. Subject Index.

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