Mathematics Books

Book SynopsisClarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analyTrade Review"The book is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The second edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions." (Zentralblatt MATH, 1 June 2015)Table of ContentsPREFACE TO SECOND EDITION xv PREFACE TO FIRST EDITION xvii 1 Representation and Geometry of Multivariate Data 1 1.1 Introduction 1 1.2 Historical Perspective 4 1.3 Graphical Display of Multivariate Data Points 5 1.3.1 Multivariate Scatter Diagrams 5 1.3.2 Chernoff Faces 11 1.3.3 Andrews’ Curves and Parallel Coordinate Curves 12 1.3.4 Limitations 14 1.4 Graphical Display of Multivariate Functionals 16 1.4.1 Scatterplot Smoothing by Density Function 16 1.4.2 Scatterplot Smoothing by Regression Function 18 1.4.3 Visualization of Multivariate Functions 19 1.4.3.1 Visualizing Multivariate Regression Functions 24 1.4.4 Overview of Contouring and Surface Display 26 1.5 Geometry of Higher Dimensions 28 1.5.1 Polar Coordinates in d Dimensions 28 1.5.2 Content of Hypersphere 29 1.5.3 Some Interesting Consequences 30 1.5.3.1 Sphere Inscribed in Hypercube 30 1.5.3.2 Hypervolume of a Thin Shell 30 1.5.3.3 Tail Probabilities of Multivariate Normal 31 1.5.3.4 Diagonals in Hyperspace 31 1.5.3.5 Data Aggregate Around Shell 32 1.5.3.6 Nearest Neighbor Distances 32 Problems 33 2 Nonparametric Estimation Criteria 36 2.1 Estimation of the Cumulative Distribution Function 37 2.2 Direct Nonparametric Estimation of the Density 39 2.3 Error Criteria for Density Estimates 40 2.3.1 MISE for Parametric Estimators 42 2.3.1.1 Uniform Density Example 42 2.3.1.2 General Parametric MISE Method with Gaussian Application 43 2.3.2 The L1 Criterion 44 2.3.2.1 L1 versus L2 44 2.3.2.2 Three Useful Properties of the L1 Criterion 44 2.3.3 Data-Based Parametric Estimation Criteria 46 2.4 Nonparametric Families of Distributions 48 2.4.1 Pearson Family of Distributions 48 2.4.2 When Is an Estimator Nonparametric? 49 Problems 50 3 Histograms: Theory and Practice 51 3.1 Sturges’ Rule for Histogram Bin-Width Selection 51 3.2 The L2 Theory of Univariate Histograms 53 3.2.1 Pointwise Mean Squared Error and Consistency 53 3.2.2 Global L2 Histogram Error 56 3.2.3 Normal Density Reference Rule 59 3.2.3.1 Comparison of Bandwidth Rules 59 3.2.3.2 Adjustments for Skewness and Kurtosis 60 3.2.4 Equivalent Sample Sizes 62 3.2.5 Sensitivity of MISE to Bin Width 63 3.2.5.1 Asymptotic Case 63 3.2.5.2 Large-Sample and Small-Sample Simulations 64 3.2.6 Exact MISE versus Asymptotic MISE 65 3.2.6.1 Normal Density 66 3.2.6.2 Lognormal Density 68 3.2.7 Influence of Bin Edge Location on MISE 69 3.2.7.1 General Case 69 3.2.7.2 Boundary Discontinuities in the Density 69 3.2.8 Optimally Adaptive Histogram Meshes 70 3.2.8.1 Bounds on MISE Improvement for Adaptive Histograms 71 3.2.8.2 Some Optimal Meshes 72 3.2.8.3 Null Space of Adaptive Densities 72 3.2.8.4 Percentile Meshes or Adaptive Histograms with Equal Bin Counts 73 3.2.8.5 Using Adaptive Meshes versus Transformation 74 3.2.8.6 Remarks 75 3.3 Practical Data-Based Bin Width Rules 76 3.3.1 Oversmoothed Bin Widths 76 3.3.1.1 Lower Bounds on the Number of Bins 76 3.3.1.2 Upper Bounds on Bin Widths 78 3.3.2 Biased and Unbiased CV 79 3.3.2.1 Biased CV 79 3.3.2.2 Unbiased CV 80 3.3.2.3 End Problems with BCV and UCV 81 3.3.2.4 Applications 81 3.4 L2 Theory for Multivariate Histograms 83 3.4.1 Curse of Dimensionality 85 3.4.2 A Special Case: d = 2 with Nonzero Correlation 87 3.4.3 Optimal Regular Bivariate Meshes 88 3.5 Modes and Bumps in a Histogram 89 3.5.1 Properties of Histogram “Modes” 91 3.5.2 Noise in Optimal Histograms 92 3.5.3 Optimal Histogram Bandwidths for Modes 93 3.5.4 A Useful Bimodal Mixture Density 95 3.6 Other Error Criteria: L1,L4,L6,L8, and L∞ 96 3.6.1 Optimal L1 Histograms 96 3.6.2 Other LP Criteria 97 Problems 97 4 Frequency Polygons 100 4.1 Univariate Frequency Polygons 101 4.1.1 Mean Integrated Squared Error 101 4.1.2 Practical FP Bin Width Rules 104 4.1.3 Optimally Adaptive Meshes 107 4.1.4 Modes and Bumps in a Frequency Polygon 109 4.2 Multivariate Frequency Polygons 110 4.3 Bin Edge Problems 113 4.4 Other Modifications of Histograms 114 4.4.1 Bin Count Adjustments 114 4.4.1.1 Linear Binning 114 4.4.1.2 Adjusting FP Bin Counts to Match Histogram Areas 117 4.4.2 Polynomial Histograms 117 4.4.3 How Much Information Is There in a Few Bins? 120 Problems 122 5 Averaged Shifted Histograms 125 5.1 Construction 126 5.2 Asymptotic Properties 128 5.3 The Limiting ASH as a Kernel Estimator 133 Problems 135 6 Kernel Density Estimators 137 6.1 Motivation for Kernel Estimators 138 6.1.1 Numerical Analysis and Finite Differences 138 6.1.2 Smoothing by Convolution 139 6.1.3 Orthogonal Series Approximations 140 6.2 Theoretical Properties: Univariate Case 142 6.2.1 MISE Analysis 142 6.2.2 Estimation of Derivatives 144 6.2.3 Choice of Kernel 145 6.2.3.1 Higher Order Kernels 145 6.2.3.2 Optimal Kernels 151 6.2.3.3 Equivalent Kernels 153 6.2.3.4 Higher Order Kernels and Kernel Design 155 6.2.3.5 Boundary Kernels 157 6.3 Theoretical Properties: Multivariate Case 161 6.3.1 Product Kernels 162 6.3.2 General Multivariate Kernel MISE 164 6.3.3 Boundary Kernels for Irregular Regions 167 6.4 Generality of the Kernel Method 167 6.4.1 Delta Methods 167 6.4.2 General Kernel Theorem 168 6.4.2.1 Proof of General Kernel Result 168 6.4.2.2 Characterization of a Nonparametric Estimator 169 6.4.2.3 Equivalent Kernels of Parametric Estimators 171 6.5 Cross-Validation 172 6.5.1 Univariate Data 172 6.5.1.1 Early Efforts in Bandwidth Selection 173 6.5.1.2 Oversmoothing 176 6.5.1.3 Unbiased and Biased Cross-Validation 177 6.5.1.4 Bootstrapping Cross-Validation 181 6.5.1.5 Faster Rates and PI Cross-Validation 184 6.5.1.6 Constrained Oversmoothing 187 6.5.2 Multivariate Data 190 6.5.2.1 Multivariate Cross-Validation 190 6.5.2.2 Multivariate Oversmoothing Bandwidths 191 6.5.2.3 Asymptotics of Multivariate Cross-Validation 192 6.6 Adaptive Smoothing 193 6.6.1 Variable Kernel Introduction 193 6.6.2 Univariate Adaptive Smoothing 195 6.6.2.1 Bounds on Improvement 195 6.6.2.2 Nearest-Neighbor Estimators 197 6.6.2.3 Sample-Point Adaptive Estimators 198 6.6.2.4 Data Sharpening 200 6.6.3 Multivariate Adaptive Procedures 202 6.6.3.1 Pointwise Adapting 202 6.6.3.2 Global Adapting 203 6.6.4 Practical Adaptive Algorithms 204 6.6.4.1 Zero-Bias Bandwidths for Tail Estimation 204 6.6.4.2 UCV for Adaptive Estimators 208 6.7 Aspects of Computation 209 6.7.1 Finite Kernel Support and Rounding of Data 210 6.7.2 Convolution and Fourier Transforms 210 6.7.2.1 Application to Kernel Density Estimators 211 6.7.2.2 FFTs 212 6.7.2.3 Discussion 212 6.8 Summary 213 Problems 213 7 The Curse of Dimensionality and Dimension Reduction 217 7.1 Introduction 217 7.2 Curse of Dimensionality 220 7.2.1 Equivalent Sample Sizes 220 7.2.2 Multivariate L1 Kernel Error 222 7.2.3 Examples and Discussion 224 7.3 Dimension Reduction 229 7.3.1 Principal Components 229 7.3.2 Projection Pursuit 231 7.3.3 Informative Components Analysis 234 7.3.4 Model-Based Nonlinear Projection 239 Problems 240 8 Nonparametric Regression and Additive Models 241 8.1 Nonparametric Kernel Regression 242 8.1.1 The Nadaraya–Watson Estimator 242 8.1.2 Local Least-Squares Polynomial Estimators 243 8.1.2.1 Local Constant Fitting 243 8.1.2.2 Local Polynomial Fitting 244 8.1.3 Pointwise Mean Squared Error 244 8.1.4 Bandwidth Selection 247 8.1.5 Adaptive Smoothing 247 8.2 General Linear Nonparametric Estimation 248 8.2.1 Local Polynomial Regression 248 8.2.2 Spline Smoothing 250 8.2.3 Equivalent Kernels 252 8.3 Robustness 253 8.3.1 Resistant Estimators 254 8.3.2 Modal Regression 254 8.3.3 L1 Regression 257 8.4 Regression in Several Dimensions 259 8.4.1 Kernel Smoothing and WARPing 259 8.4.2 Additive Modeling 261 8.4.3 The Curse of Dimensionality 262 8.5 Summary 265 Problems 266 9 Other Applications 267 9.1 Classification, Discrimination, and Likelihood Ratios 267 9.2 Modes and Bump Hunting 273 9.2.1 Confidence Intervals 273 9.2.2 Oversmoothing for Derivatives 275 9.2.3 Critical Bandwidth Testing 275 9.2.4 Clustering via Mixture Models and Modes 277 9.2.4.1 Gaussian Mixture Modeling 277 9.2.4.2 Modes for Clustering 280 9.3 Specialized Topics 286 9.3.1 Bootstrapping 286 9.3.2 Confidence Intervals 287 9.3.3 Survival Analysis 289 9.3.4 High-Dimensional Holes 290 9.3.5 Image Enhancement 292 9.3.6 Nonparametric Inference 292 9.3.7 Final Vignettes 293 9.3.7.1 Principal Curves and Density Ridges 293 9.3.7.2 Time Series Data 294 9.3.7.3 Inverse Problems and Deconvolution 294 9.3.7.4 Densities on the Sphere 294 Problems 294 APPENDIX A Computer Graphics in R3 296 A.1 Bivariate and Trivariate Contouring Display 296 A.1.1 Bivariate Contouring 296 A.1.2 Trivariate Contouring 299 A.2 Drawing 3-D Objects on the Computer 300 APPENDIX B DataSets 302 B.1 US Economic Variables Dataset 302 B.2 University Dataset 304 B.3 Blood Fat Concentration Dataset 305 B.4 Penny Thickness Dataset 306 B.5 Gas Meter Accuracy Dataset 307 B.6 Old Faithful Dataset 309 B.7 Silica Dataset 309 B.8 LRL Dataset 310 B.9 Buffalo Snowfall Dataset 310 APPENDIX C Notation and Abbreviations 311 C.1 General Mathematical and Probability Notation 311 C.2 Density Abbreviations 312 C.3 Error Measure Abbreviations 313 C.4 Smoothing Parameter Abbreviations 313 REFERENCES 315 AUTHOR INDEX 334 SUBJECT INDEX 339

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Book SynopsisExperimentation is one of the most common activities in which all people engage. In this thoroughly updated Second Edition, Experiments presents the most modern, up-to-date treatment in the design and analysis of experiment topics currently available.Trade Review“On the whole, I think the book is ideal for a year-long course at the graduate level (there is much more material in the book than can be reasonably covered even in a year-long course), but is still advanced for undergraduates.” (Zentralblatt MATH, 2012) Table of ContentsPreface to the Second Edition. Preface to the First Edition. Suggestions of Topics for Instructors. List of Experiments and Data Sets. 1 Basic Concepts for Experimental Design and Introductory Regression Analysis. 1.1 Introduction and Historical Perspective. 1.2 A Systematic Approach to the Planning and Implementation of Experiments. 1.3 Fundamental Principles: Replication, Randomization, and Blocking. 1.4 Simple Linear Regression. 1.5 Testing of Hypothesis and Interval Estimation. 1.6 Multiple Linear Regression. 1.7 Variable Selection in Regression Analysis. 1.8 Analysis of Air Pollution Data. 1.9 Practical Summary. 2 Experiments with a Single Factor. 2.1 One-Way Layout. 2.2 Multiple Comparisons. 2.3 Quantitative Factors and Orthogonal Polynomials. 2.4 Expected Mean Squares and Sample Size Determination. 2.5 One-Way Random Effects Model. 2.6 Residual Analysis: Assessment of Model Assumptions. 2.7 Practical Summary. 3 Experiments with More Than One Factor. 3.1 Paired Comparison Designs. 3.2 Randomized Block Designs. 3.3 Two-Way Layout: Factors With Fixed Levels. 3.4 Two-Way Layout: Factors With Random Levels. 3.5 Multi-Way Layouts. 3.6 Latin Square Designs: Two Blocking Variables. 3.7 Graeco-Latin Square Designs. 3.8 Balanced Incomplete Block Designs. 3.9 Split-Plot Designs. 3.10 Analysis of Covariance: Incorporating Auxiliary Information. 3.11 Transformation of the Response. 3.12 Practical Summary. 4 Full Factorial Experiments at Two Levels. 4.1 An Epitaxial Layer Growth Experiment. 4.2 Full Factorial Designs at Two Levels: A General Discussion. 4.3 Factorial Effects and Plots. 4.4 Using Regression to Compute Factorial Effects. 4.5 ANOVA Treatment of Factorial Effects. 4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity. 4.7 Comparisons with the "One-Factor-at-a-Time" Approach. 4.8 Normal and Half-Normal Plots for Judging Effect Significance. 4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates. 4.10 Nominal-the-Best Problem and Quadratic Loss Function. 4.11 Use of Log Sample Variance for Dispersion Analysis. 4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment. 4.13 Test of Variance Homogeneity and Pooled Estimate of Variance. 4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments with Variance Estimates. 4.15 Blocking and Optimal Arrangement of 2k Factorial Designs in 2q Blocks. 4.16 Practical Summary. 5 Fractional Factorial Experiments at Two Levels. 5.1 A Leaf Spring Experiment. 5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria Of Resolution and Minimum Aberration. 5.3 Analysis of Fractional Factorial Experiments. 5.4 Techniques for Resolving the Ambiguities in Aliased Effects. 5.5 Selection of 2k-p Designs Using Minimum Aberration and Related Criteria. 5.6 Blocking in Fractional Factorial Designs. 5.7 Practical Summary. 6 Full Factorial and Fractional Factorial Experiments at Three Levels. 6.1 A Seat-Belt Experiment. 6.2 Larger-the-Better and Smaller-the-Better Problems. 6.3 3k Full Factorial Designs. 6.4 3k-p Fractional Factorial Designs. 6.5 Simple Analysis Methods: Plots and Analysis of Variance. 6.6 An Alternative Analysis Method. 6.7 Analysis Strategies for Multiple Responses I: Out-of-Spec Probabilities. 6.8 Blocking in 3k and 3k-p Designs. 6.9 Practical Summary. 7 Other Design and Analysis Techniques for Experiments at More Than Two Levels. 7.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design. 7.2 Method of Replacement and Construction of 2m4n Designs. 7.3 Minimum Aberration 2m4n Designs with n = 1, 2. 7.4 An Analysis Strategy for 2m4n Experiments. 7.5 Analysis of the Router Bit Experiment. 7.6 A Paint Experiment Based on a Mixed Two-Level and Three-Level Design. 7.7 Design and Analysis of 36-Run Experiments at Two And Three Levels. 7.8 rk-p Fractional Factorial Designs for any Prime Number r. 7.9 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling. 7.10 Practical Summary. 8 Nonregular Designs: Construction and Properties. 8.1 Two Experiments: Weld-Repaired Castings and Blood Glucose Testing. 8.2 Some Advantages of Nonregular Designs Over the 2k-p and 3k-p Series of Designs. 8.3 A Lemma on Orthogonal Arrays. 8.4 Plackett-Burman Designs and Hall's Designs. 8.5 A Collection of Useful Mixed-Level Orthogonal Arrays. 8.6 Construction of Mixed-Level Orthogonal Arrays Based on Difference Matrices. 8.7 Construction of Mixed-Level Orthogonal Arrays Through the Method of Replacement. 8.8 Orthogonal Main-Effect Plans Through Collapsing Factors. 8.9 Practical Summary. 9 Experiments with Complex Aliasing. 9.1 Partial Aliasing of Effects and the Alias Matrix. 9.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis. 9.3 Simplification of Complex Aliasing via Effect Sparsity. 9.4 An Analysis Strategy for Designs with Complex Aliasing. 9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing. 9.6 Supersaturated Designs: Design Construction and Analysis. 9.7 Practical Summary. 10 Response Surface Methodology. 10.1 A Ranitidine Separation Experiment. 10.2 Sequential Nature of Response Surface Methodology. 10.3 From First-Order Experiments to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid Search. 10.4 Analysis of Second-Order Response Surfaces. 10.5 Analysis of the Ranitidine Experiment. 10.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions. 10.7 Central Composite Designs. 10.8 Box-Behnken Designs and Uniform Shell Designs. 10.9 Practical Summary. 11 Introduction to Robust Parameter Design. 11.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments. 11.2 Strategies for Reducing Variation. 11.3 Noise (Hard-to-Control) Factors. 11.4 Variation Reduction Through Robust Parameter Design. 11.5 Experimentation and Modeling Strategies I: Cross Array. 11.6 Experimentation and Modeling Strategies II: Single Array and Response Modeling. 11.7 Cross Arrays: Estimation Capacity and Optimal Selection. 11.8 Choosing Between Cross Arrays and Single Arrays. 11.9 Signal-to-Noise Ratio and Its Limitations for Parameter Design Optimization. 11.10 Further Topics. 11.11 Practical Summary. 12 Robust Parameter Design for Signal-Response Systems. 12.1 An Injection Molding Experiment. 12.2 Signal-Response Systems and their Classification. 12.3 Performance Measures for Parameter Design Optimization. 12.4 Modeling and Analysis Strategies. 12.5 Analysis of the Injection Molding Experiment. 12.6 Choice of Experimental Plans. 12.7 Practical Summary. 13 Experiments for Improving Reliability. 13.1 Experiments with Failure Time Data. 13.2 Regression Model for Failure Time Data. 13.3 A Likelihood Approach for Handling Failure Time Data with Censoring. 13.4 Design-Dependent Model Selection Strategies. 13.5 A Bayesian Approach to Estimation and Model Selection for Failure Time Data. 13.6 Analysis of Reliability Experiments with Failure Time Data. 13.7 Other Types of Reliability Data. 13.8 Practical Summary. 14 Analysis of Experiments with Nonnormal Data. 14.1 A Wave Soldering Experiment with Count Data. 14.2 Generalized Linear Models. 14.3 Likelihood-Based Analysis of Generalized Linear Models. 14.4 Likelihood-Based Analysis of the Wave Soldering Experiment. 14.5 Bayesian Analysis of Generalized Linear Models. 14.6 Bayesian Analysis of the Wave Soldering Experiment. 14.7 Other Uses and Extensions of Generalized Linear Models and Regression Models for Nonnormal Data. 14.8 Modeling and Analysis for Ordinal Data. 14.9 Analysis of Foam Molding Experiment. 14.10 Scoring: A Simple Method for Analyzing Ordinal Data. 14.11 Practical Summary. Appendix A Upper Tail Probabilities of the Standard Normal Distribution. Appendix B Upper Percentiles of the t Distribution. Appendix C Upper Percentiles of the χ2 Distribution. Appendix D Upper Percentiles of the F Distribution. Appendix E Upper Percentiles of the Studentized Range Distribution. Appendix F Upper Percentiles of the Studentized Maximum Modulus Distribution. Appendix G Coefficients of Orthogonal Contrast Vectors. Appendix H Critical Values for Lenth's Method. Author Index. Subject Index.

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Book SynopsisIncorporates the many tools needed for modeling and pricing in finance and insurance Introductory Stochastic Analysis for Finance and Insurance introduces readers to the topics needed to master and use basic stochastic analysis techniques for mathematical finance.Trade Review"…a positive addition to the introductory textbooks on stochastic calculus for financial applications." (Journal of the American Statistical Association, June 2007) "The actuarial topics discussed by the author are absolutely nonstandard and provide a strong point in favor of this book compared to its competitors…" (Mathematical Reviews, Issue 2007a) "…very suitable for practitioners who just need to understand the main connections between some financial concepts and stochastic analysis." (MAA Reviews, April 30, 2006)Table of ContentsList of Figures. List of Tables. Preface. 1. Introduction. 2. Overview of Probability Theory. 3. Discrete-Time Stochastic Processes. 4. Continuous-Time Stochastic Processes. 5. Stochastic Calculus: Basic Topics. 6. Stochastic Calculus: Advanced Topics. 7. Applications in Insurance. References. Topic Index.

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Book SynopsisThe material in the book is intended for a first course on applied probability and statistics for engineering students at the sophomore or junior level, or for self study, stressing probabilistic modeling and the fundamentals of statistical inferences. The primary aim is to provide an in-depth understanding of the fundamentals for the proper application in engineering problems. The second edition of this well-known book (previously titled Probability Concepts in Engineering Planning and Design) by Alfredo Ang and Wilson Tang, two world-renowned educators, has been revised to simplify understanding the fundamentals of probability and statistics for engineering students. The second edition includes many new and expanded topics, including hypothesis testing and confidence intervals in regression analysis. Students using this text will develop the ability to formulate and solve real-world problems in engineering. The authors accomplish this by explaining all the concepts and metTable of ContentsChapter 1 - Role of Probability and Statistics in Engineering Chapter 2 -- Fundamentals of Probability Models Chapter 3 -- Analytical Models of Random Phenomena Chapter 4 -- Functions of Random Variables Chapter 5 - Computer-Based Numerical and Simulation Methods in Probability Chapter 6 -- Statistical Inferences from Observational Data Chapter 7 -- Determination of Probability Distribution Models Chapter 8 -- Regression and Correlation Analyses Chapter 9 -- The Bayesian Approach Chapter 10 - Elements of Quality Assurance and Acceptance Sampling (Available only online at the Wiley web site) Appendices: Table A.1 -- Standard Normal Probabilities Table A.2 - CDF of the Binomial Distribution Table A.3 - Critical Values of t Distribution at Confidence Level (1- a)=p Table A.4 - Critical Values of the c2 Distribution at Confidence Level (1-a)=pTable A.5 - Critical Values of Dna at Significance Level a in the K-S Test Table A.6 - Critical Values of the Anderson-Darling Goodness-of-fit Test (for 4 specific distributions)

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Book SynopsisThis book reflects the most recent developments in finite element methods and is accessible to students of mathematics, engineering, and physics due to its approach. An overview and consolidation of the basic knowledge of linear spaces and PDEs is presented, which is followed by this discussion of finite element methods and its applications.Trade Review"This well-written book discusses the modern methods of partial differential equations and the finite element methods…recommended." (CHOICE, July 2006)Table of ContentsList of Figures. List of Tables. Preface. Acknowldegments. 1. Partial Differential Equations. 2. Continuous Elements for 1D Problems. 3. General Concept of Nodal Elements. 4. Continuous Finite Elements for 2D Problems. 5. Transient Problems and ODE Solvers. 6. Beam and Plate Bending Problems. 7. Equations of Electrimagnetics. Appendix A: Basics of Functional Analysis. Appendix B: Software and Examples. References. Index.

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Book SynopsisA comprehensive solutions manual for students using the Vector Calculus text This book gives a comprehensive and thorough introduction to ideas and major results of the theory of functions of several variables and of modern vector calculus in two and three dimensions. Clear and easy-to-follow writing style, carefully crafted examples, wide spectrum of applications and numerous illustrations, diagrams, and graphs invite students to use the textbook actively, helping them to both enforce their understanding of the material and to brush up on necessary technical and computational skills. The Student Solutions Manual to Accompany Vector Calculus also pays particular attention to material that some students find challenging, such as the chain rule, Implicit Function Theorem, parametrizations, or the Change of Variables Theorem.

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Book SynopsisThis user-friendly new edition reflects a modern and accessible approach to experimental design and analysis Design and Analysis of Experiments, Volume 1, Second Edition provides a general introduction to the philosophy, theory, and practice of designing scientific comparative experiments and also details the intricacies that are often encountered throughout the design and analysis processes. With the addition of extensive numerical examples and expanded treatment of key concepts, this book further addresses the needs of practitioners and successfully provides a solid understanding of the relationship between the quality of experimental design and the validity of conclusions. This Second Edition continues to provide the theoretical basis of the principles of experimental design in conjunction with the statistical framework within which to apply the fundamental concepts. The difference between experimental studies and observational studies is addressed, along with a discussioTrade Review“This user-friendly new edition reflects a modern and accessible approach to experimental design and analysis.” (Landtechnik, 1 November 2012) "This book is an ideal textbook for graduate courses in experimental design and also a practical reference book for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering and business." (Biometrical Journal, August 2008) "The revisions, reorganization, and addition certainly enhance the value of this edition. Like the first edition, the current edition will continue to play an important role in the arena of statistical design of experiments." (Technometrics, November 2008) "The revisions, reorganization, and addition certainly enhance the value of this edition. Like the first edition, the current edition will continue to play an important role in the arena of statistical design of experiments." (Technometrics, Nov 2008) "This book is an ideal textbook for graduate courses in experimental design and also a practical reference book for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering and business." (Biometrical Journal, Aug 2008)Table of Contents1. The Processes of Science. 1.1 Introduction. 1.2 Development of Theory. 1.3 The Nature and Role of Theory in Science. 1.4 Varieties of Theory. 1.5 The Problem of General Science. 1.6 Causality. 1.7 The Upshot. 1.8 What Is An Experiment?. 1.9 Statistical Inference. 2. Principles of Experimental Design. 2.1 Confirmatory and Exploratory Experiments. 2.2 Steps of Designed Investigations. 2.3 The Linear Model. 2.4 Illustrating Individual Steps: Study 1. 2.5 Three Principles of Experimental Design. 2.6 The Statistical Triangle and Study 2. 2.7 Planning the Experiment. 2.8 Cooperation between Scientist and Statistician. 2.9 General Principle of Inference. 2.10 Other Considerations for Experimental Designs. 3. Survey of Designs and Analyses. 3.1 Introduction. 3.2 Error-Control Designs. 3.3 Treatment Designs. 3.4 Combining Ideas. 3.5 Sampling Designs. 3.6 Analysis and Statistical Software. 3.7 Summary. 4. Linear Model Theory. 4.1 Introduction. 4.2 Representation of Linear Models. 4.3 Functional and Classificatory Linear Models. 4.4 The Fitting Of Y .= X_. 4.5 The Moore-Penrose Generalized Inverse. 4.6 The Conditioned Linear Model. 4.7 The Two-Part Linear Model. 4.8 A Special Case of a Partitioned Model. 4.9 Three-Part Models. 4.10 The Two-Way Classification Without Interaction. 4.11 The K-Part Linear Model. 4.12 Balanced Classificatory Structures. 4.13 Unbalanced Data Structures. 4.14 Analysis of Covariance Model. 4.15 From Data Analysis to Statistical Inference. 4.16 The Simple Normal Stochastic Linear Model. 4.17 Distribution Theory with GMNLM. 4.18 Mixed Models. 5. Randomization. 5.1 Introduction. 5.2 The Tea Tasting Lady. 5.3 A Triangular Experiment. 5.4 The Simple Arithmetical Experiment. 5.5 Randomization Ideas for Intervention Experiments. 5.6 The General Idea of the Experiment Randomization Test. 5.7 Introduction to Subsequent. 6. The Completely Randomized Design. 6.1 Introduction and Definition. 6.2 The Randomization Process. 6.3 The Derived Linear Model. 6.4 Analysis Of Variance. 6.5 Statistical Tests. 6.6 Approximating the Randomization Test. 6.7 CRD with Unequal Numbers of Replications. 6.8 Number of Replications. 6.9 Subsampling In A CRD. 6.10 Transformations. 6.11 Examples Using SASR. 7. Comparisons of Treatments. 7.1 Introduction. 7.2 Comparisons for Qualitative Treatments. 7.3 Orthogonality and Orthogonal Comparisons. 7.4 Comparisons for Quantitative Treatments. 7.5 Multiple Comparison Procedures. 7.6 Grouping Treatments. 7.7 Examples Using SAS. 8. Use of Supplementary Information. 8.1 Introduction. 8.2 Motivation of the Procedure. 8.3 Analysis of Covariance Procedure. 8.4 Treatment Comparisons. 8.5 Violation of Assumptions. 8.6 Analysis of Covariance with Subsampling. 8.7 The Case of Several Covariates. 8.8 Examples Using SASR. 9. Randomized Block Designs. 9.1 Introduction. 9.2 Randomized Complete Block Design. 9.3 Relative Efficiency of the RCBD. 9.4 Analysis of Covariance. 9.5 Missing Observations. 9.6 Nonadditivity in the RCBD. 9.7 The Generalized Randomized Block Design. 9.8 Incomplete Block Designs. 9.9 Systematic Block Designs. 9.10 Examples Using SASR. 10. Latin Square Type Designs. 10.1 Introduction and Motivation. 10.2 Latin Square Design. 10.3 Replicated Latin Squares. 10.4 Latin Rectangles. 10.5 Incomplete Latin Squares. 10.6 Orthogonal Latin Squares. 10.7 Change-Over Designs. 10.8 Examples Using SAS. 11. Factorial Experiments: Basic Ideas. 11.1 Introduction. 11.2 Inferences from Factorial Experiments. 11.3 Experiments with Factors at Two Levels. 11.4 The Interpretation of Effects and Interactions. 11.5 Interactions: A Case Study. 11.6 2n Factorials in Incomplete Blocks. 11.7 Fractions of 2n Factorials. 11.8 Orthogonal Main Effect Plans for 2n Factorials. 11.9 Experiments with Factors at Three Levels. 11.10experimentswith Factors at Two and Three Levels. 11.11examples Using SAS. 12. Response Surface Designs. 12.1 Introduction. 12.2 Formulation of the Problem. 12.3 First-Order Models and Designs. 12.4 Second-Order Models and Designs. 12.5 Integrated Mean Squared Error Designs. 12.6 Searching For an Optimum. 12.7 Experiments with Mixtures. 12.8 Examples Using SAS. 13. Split-Plot Type Designs. 13.1 Introduction. 13.2 The Simple Split-Plot Design. 13.3 Relative Efficiency of Split-Plot Design. 13.4 Other Forms of Split-Plot Designs. 13.5 Split-Block Design. 13.6 The Split-Split-Plot Design. 13.7 Examples Using SAS. 14. Designs with Repeated Measures. 14.1 Introduction. 14.2 Methods for Analyzing Repeated Measures Data. 14.3 Examples Using SAS. 14.4 Exercises.

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Book SynopsisThis books illustrates how data mining techniques, through the application of algorithms and graphs, have been responding to the need for the collection and storage of larger and more complex volumes of data.Trade Review"…individuals with no background analyzing graph data can learn how to represent the data as graphs, extract patterns or concepts from the data, and see how researchers apply the methodologies to real datasets." (Computing Reviews.com, March 23, 2007)Table of ContentsPreface. Acknowledgments. Contributors. 1 INTRODUCTION (Lawrence B. Holder and Diane J. Cook). 1.1 Terminology. 1.2 Graph Databases. 1.3 Book Overview. References. Part I GRAPHS. 2 GRAPH MATCHING—EXACT AND ERROR-TOLERANT METHODS AND THE AUTOMATIC LEARNING OF EDIT COSTS (Horst Bunke and Michel Neuhaus). 2.1 Introduction. 2.2 Definitions and Graph Matching Methods. 2.3 Learning Edit Costs. 2.4 Experimental Evaluation. 2.5 Discussion and Conclusions. References. 3 GRAPH VISUALIZATION AND DATA MINING (Walter Didimo and Giuseppe Liotta). 3.1 Introduction. 3.2 Graph Drawing Techniques. 3.3 Examples of Visualization Systems. 3.4 Conclusions. References. 4 GRAPH PATTERNS AND THE R-MAT GENERATOR (Deepayan Chakrabarti and Christos Faloutsos). 4.1 Introduction. 4.2 Background and Related Work. 4.3 NetMine and R-MAT. 4.4 Experiments. 4.5 Conclusions. References. Part II MINING TECHNIQUES. 5 DISCOVERY OF FREQUENT SUBSTRUCTURES (Xifeng Yan and Jiawei Han). 5.1 Introduction. 5.2 Preliminary Concepts. 5.3 Apriori-based Approach. 5.4 Pattern Growth Approach. 5.5 Variant Substructure Patterns. 5.6 Experiments and Performance Study. 5.7 Conclusions. References. 6 FINDING TOPOLOGICAL FREQUENT PATTERNS FROM GRAPH DATASETS (Michihiro Kuramochi and George Karypis). 6.1 Introduction. 6.2 Background Definitions and Notation. 6.3 Frequent Pattern Discovery from Graph Datasets—Problem Definitions. 6.4 FSG for the Graph-Transaction Setting. 6.5 SIGRAM for the Single-Graph Setting. 6.6 GREW—Scalable Frequent Subgraph Discovery Algorithm. 6.7 Related Research. 6.8 Conclusions. References. 7 UNSUPERVISED AND SUPERVISED PATTERN LEARNING IN GRAPH DATA (Diane J. Cook, Lawrence B. Holder, and Nikhil Ketkar). 7.1 Introduction. 7.2 Mining Graph Data Using Subdue. 7.3 Comparison to Other Graph-Based Mining Algorithms. 7.4 Comparison to Frequent Substructure Mining Approaches. 7.5 Comparison to ILP Approaches. 7.6 Conclusions. References. 8 GRAPH GRAMMAR LEARNING (Istvan Jonyer). 8.1 Introduction. 8.2 Related Work. 8.3 Graph Grammar Learning. 8.4 Empirical Evaluation. 8.5 Conclusion. References. 9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio). 9.1 Introduction. 9.2 Graph-Based Induction Revisited. 9.3 Problem Caused by Chunking in B-GBI. 9.4 Chunkingless Graph-Based Induction (Cl-GBI). 9.5 Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI). 9.6 Conclusions. References. 10 SOME LINKS BETWEEN FORMAL CONCEPT ANALYSIS AND GRAPH MINING (Michel Liquière). 10.1 Presentation. 10.2 Basic Concepts and Notation. 10.3 Formal Concept Analysis. 10.4 Extension Lattice and Description Lattice Give Concept Lattice. 10.5 Graph Description and Galois Lattice. 10.6 Graph Mining and Formal Propositionalization. 10.7 Conclusion. References. 11 KERNEL METHODS FOR GRAPHS (Thomas Gärtner, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel). 11.1 Introduction. 11.2 Graph Classification. 11.3 Vertex Classification. 11.4 Conclusions and Future Work. References. 12 KERNELS AS LINK ANALYSIS MEASURES (Masashi Shimbo and Takahiko Ito). 12.1 Introduction. 12.2 Preliminaries. 12.3 Kernel-based Unified Framework for Importance and Relatedness. 12.4 Laplacian Kernels as a Relatedness Measure. 12.5 Practical Issues. 12.6 Related Work. 12.7 Evaluation with Bibliographic Citation Data. 12.8 Summary. References. 13 ENTITY RESOLUTION IN GRAPHS (Indrajit Bhattacharya and Lise Getoor). 13.1 Introduction. 13.2 Related Work. 13.3 Motivating Example for Graph-Based Entity Resolution. 13.4 Graph-Based Entity Resolution: Problem Formulation. 13.5 Similarity Measures for Entity Resolution. 13.6 Graph-Based Clustering for Entity Resolution. 13.7 Experimental Evaluation. 13.8 Conclusion. References. Part III APPLICATIONS. 14 MINING FROM CHEMICAL GRAPHS (Takashi Okada). 14.1 Introduction and Representation of Molecules. 14.2 Issues for Mining. 14.3 CASE: A Prototype Mining System in Chemistry. 14.4 Quantitative Estimation Using Graph Mining. 14.5 Extension of Linear Fragments to Graphs. 14.6 Combination of Conditions. 14.7 Concluding Remarks. References. 15 UNIFIED APPROACH TO ROOTED TREE MINING: ALGORITHMS AND APPLICATIONS (Mohammed Zaki). 15.1 Introduction. 15.2 Preliminaries. 15.3 Related Work. 15.4 Generating Candidate Subtrees. 15.5 Frequency Computation. 15.6 Counting Distinct Occurrences. 15.7 The SLEUTH Algorithm. 15.8 Experimental Results. 15.9 Tree Mining Applications in Bioinformatics. 15.10 Conclusions. References. 16 DENSE SUBGRAPH EXTRACTION (Andrew Tomkins and Ravi Kumar). 16.1 Introduction. 16.2 Related Work. 16.3 Finding the densest subgraph. 16.4 Trawling. 16.5 Graph Shingling. 16.6 Connection Subgraphs. 16.7 Conclusions. References. 17 SOCIAL NETWORK ANALYSIS (Sherry E. Marcus, Melanie Moy, and Thayne Coffman). 17.1 Introduction. 17.2 Social Network Analysis. 17.3 Group Detection. 17.4 Terrorist Modus Operandi Detection System. 17.5 Computational Experiments. 17.6 Conclusion. References. Index.

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Book SynopsisLearn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote.Trade Review“…the books have plenty of wise advice for the application of statistics…” (Bulletin of Mathematical Biology,2007)Table of ContentsPreface. 1. Variation (or What Statistics Is All About). 2. Probability. 3. Distributions. 4. Testing Hypotheses. 5. Designing an Experiment or Survey. 6. Analyzing Complex Experiments. 7. Developing Models. 8. Reporting Your Findings. 9. Problem Solving. Appendix: An Microsoft Office Excel Primer. Index to Excel and Excel Add-In Functions. Subject Index.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisA highly detailed, yet readable treatment of the growing field of robust statistics--the statistics of approximate parametric models. Introducing concepts, theory, and applications, this work is designed to be accessible to a broad audience, avoiding allusions to high-powered mathematics while emphasizing ideas, heuristics, and background.Table of Contents1. Introduction and Motivation. 2. One-Dimensional Estimators. 3. One-Dimensional Tests. 4. Multidimensional Estimators. 5. Estimation of Covariance Matrices and Multivariate Location. 6. Linear Models: Robust Estimation. 7. Linear Models: Robust Testing. 8. Complements and Outlook. References. Index.

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Book SynopsisAn introduction to the practical application of cluster analysis, Finding Groups in Data presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability.Table of Contents1. Introduction. 2. Partitioning Around Medoids (Program PAM). 3. Clustering large Applications (Program CLARA). 4. Fuzzy Analysis. 5. Agglomerative Nesting (Program AGNES). 6. Divisive Analysis (Program DIANA). 7. Monothetic Analysis (Program MONA). Appendix 1. Implementation and Structure of the Programs. Appendix 2. Running the Programs. Appendix 3. Adapting the Programs to Your Needs. Appendix 4. The Program CLUSPLOT. References. Author Index. Subject Index.

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Book SynopsisThis book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.Trade Review"This book is a good introduction to neural networks for a statistician." (Journal of the American Statistical Association, March 2009) "The book provides an excellent introduction to neutral networks from a statistical perspective." (International Statistical Review, 2008) "Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering." (Mathematical Reviews) Table of ContentsNotation and Code Examples. Preface. Acknowledgments. 1. Introduction. 2. The Multi-Layer Perception Model. 3. Linear Discriminant Analysis. 4. Activation and Penalty Functions. 5. Model Fitting and Evaluation. 6. The Task-Based MLP. 7. Incorporating Spatial Information into an MLP Classifier. 8. Influence Curves for the Multi-Layer Perceptron Classifier. 9. The Sensitivity Curves of the MLP Classifier. 10. A Robust Fitting Procedure for MLP Models. 11. Smoothed Weights. 12. Translation Invariance. 13. Fixed-slope Training. Appendix A. Function Minimization. Appendix B. Maximum Values of the Influence Curve. Topic Index.

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Book SynopsisBreaking with the traditional treatment of random processes in engineering On the surface, Introduction to Random Processes in Engineering is simply a first-rate textbook for senior or first-year graduate engineering courses in stochastic processes.Table of ContentsPreface. Review. 1. Random Processes: Basic Concepts, Properties. 2. Stationary Random Processes: Covariance and Spectrum. 3. Response of Linear Systems to Random Inputs: Discrete-Time Models. 4. Response of Linear Systems to Random Inputs: Continuous-Time Models. 5. Time Averages and the Ergodic Principle. 6. Sampling Principle and Interpolation. 7. Simulation of Random Processes. 8. Random Fields. 9. Linear Filtering Theory. Index.

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Book SynopsisA state-of-the-art introduction to the powerful mathematical and statistical tools used in the field of finance The use of mathematical models and numerical techniques is a practice employed by a growing number of applied mathematicians working on applications in finance. Reflecting this development, Numerical Methods in Finance and Economics: A MATLAB?-Based Introduction, Second Edition bridges the gap between financial theory and computational practice while showing readers how to utilize MATLAB?--the powerful numerical computing environment--for financial applications. The author provides an essential foundation in finance and numerical analysis in addition to background material for students from both engineering and economics perspectives. A wide range of topics is covered, including standard numerical analysis methods, Monte Carlo methods to simulate systems affected by significant uncertainty, and optimization methods to find an optimal set of decisionsTrade Review"Inquisitive statisticians may find this book an interesting read in which to put their theories and epistemology to the test." (Journal of American Statistics, 2008) "In summary, this book is a "must have" for professionals and researchers who employ numerical methods in economic and financial modeling. The amount and quality of the material that the author offers is so generous that readers are likely to benefit from it even if they are not interested in some of the specific applications presented." (Interfaces, June 2008) "…a broad and enjoyable introduction to computational finance." (Journal of the American Statistical Association, December 2007) "...written in such a lucid way that it provides great pleasure in reading...excellent for students...of great value to practitioners who are new to the field." (MAA Reviews, November 23, 2006)Table of ContentsPreface to the Second Edition. From the Preface to the First Edition. PART I. BACKGROUND. 1. Motivation. 2. Financial Theory. PART II. NUMERICAL METHODS. 3. Basics of Numerical Analysis. 4. Numerical Integration: Deterministic and Monte Carlo Methods. 5. Finite Difference Methods for Partial Differential Equations. 6. Convex Optimization. PART III. PRICING EQUITY OPTIONS. 7. Option Pricing by Binomial and Trinomial Lattices. 8. Option Pricing by Monte Carlo Methods. 9. Option Pricing by Finite Difference Methods. PART IV. ADVANCED OPTMIZATION MODELS AND METHODS. 10. Dynamic Programming. 11. Linear Stochastic Programming Models with Recourse. 12. Non-Convex Optimization. PART V. APPENDICES. Appendix A. Introduction to MATLAB Programming. Appendix B. Refresher on Probability theory and Statistics. Appendix C. Introduction to AMPL. Index.

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Book SynopsisCulinary Calculations, Second Edition provides the mathematical knowledge and skills that are essential for a successful career in today''s competitive food service industry. This user-friendly guide starts with basic principles before introducing more specialized topics like costing, AP/EP, menu pricing, recipe conversion and costing, and inventory costs. Written in a non-technical, easy-to-understand style, the book features a case study that runs through all chapters, showing the various math concepts put into real-world practice. This revised and updated Second Edition of Culinary Calculations covers relevant math skills for four key areas: Basic math for the culinary arts and food service industry Math for the professional kitchen Math for the business side of the food service industry Computer applications for the food service industry Each chapter within these sections is rich with resources, inTable of ContentsIntroduction: Importance of Mathematics to the Food Service Industry. A. Math in the Kitchen. 1. Units of Measure. 2. Food Yield. 3. Recipes. 4. Portion. B. Math for Business Operations. 1. Profit and Non Profit Food Service. 2. Basic accounting terminology. 3. Purchasing. 4. Inventory management. 5. Menu Pricing. C. Case Study Introduction. SECTION I: BASIC MATHEMATICS FOR THE CULINARY ARTS AND FOOD SERVICE INDUSTRY. Chapter 1. Basic Mathematics with Whole Numbers. A. Addition. B. Subtraction. C. Multiplication. D. Division. Chapter 2: Applied Math Problems with Simple Solutions. Chapter 3: Mixed Numbers and Non-integer Quantities. A. Fractions. B. Decimals. C. Percents. Chapter 4: Basic Mathematical Operations with Mixed Numbers and Non-integer Quantities. A. Fractions. B. Mixed Numbers. C. Decimals. D. Percents. Chapter 5: Basic Mathematics: Additional Information and Tips for Success. A.Rounding and Estimation. B.Multipliers and Conversion Factors. C.Ratios. D.Proportion. E.Greater Than, Less Than. SECTION II: MATHEMATICS FOR THE PROFESSIONAL KITCHEN. Chapter 6: Standardized Recipes. A. Format. B. Importance of the information contained. C. Continual Case Study Steps I and II. Chapter 7: Units of Measure. A. United States Standard Units of Measure. B. Metric Units of Measure. C. Comparison of US Standard Units to Metric Units. D. Conversion of US Standard to Metric. E. Conversion of Metric to US Standard. F. Conversion of Volume to Weight. G. Conversion of Weight to Volume. H. Continual Case Study Step III. Chapter 8: Food Service Specific Terminology and Mathematics. Part I: As Purchased, Edible Portion, As Served, Yield Percent. A. As Purchased . B. Edible Portion . C. As Served Portion. D. Yield Percent. E. Average Yield Percent Chart. Chapter 9: Food Service Specific Terminology and Mathematics. Part II: The Impact of As Purchased and Edible Portion on the Major Food Groups. A. Food Purchasing. B. Food Product Groups. C. Meats. D. Yield Test. E. Produce. F. Dairy Products. G. Pasta, Rice, and Legumes. H. Miscellaneous Items. I. Edible Portion and As Served . Chapter 10: Food Service Specific Terminology and Mathematics. Part III: Recipe and Portion Costing. A. Relationship between As Purchased and Edible Portion. B. Approximate or Average Yield Percent. C. Recipe Costing using the Approximate or Average Yield Percent. D. Steps to calculate a recipe cost: Simple and Common examples. E. Miscellaneous Ingredient Cost. F. Additional costs to serve a guest a meal. G. Continual Case Study: Step IV. SECTION III: MATH FOR THE BUSINESS SIDE OF THE FOOD SERVICE INDUSTRY. Chapter 11: Menu Pricing. A. A la carte, Table d’hote, and Prix Fixe pricing. B. Food Cost and Food Cost Percent pricing. C. Limitations of Food Cost pricing. D. Additional Menu Pricing Techniques. E. Alcoholic Beverages, Alcoholic Beverage Cost and Percent . F. Alcoholic Beverage Menu Pricing. G. Bakery and Pastry Industry Pricing. E. Case Study Step V. Chapter 12: Basic Accounting for Food Service Operations also Known as The Impact of Menu Pricing on Success and Profit. A. Revenue. B. Cost. 1.The cost of energy. C. Profit and Loss. D. Case Study Step VI. Chapter 13: Labor Cost and Control Techniques. A. Labor Costs. B. Staffing Guide. C. Employee Schedules. D. Labor Cost Control. E. Case Study Step VII. Chapter 14: Purchasing and Inventory Management. A. Purchasing Food Products. B. Inventory Management. C. Inventory Quantities. D. Cost of Goods Sold. E. Inventory Turnover. E. Case Study Step VIII. SECTION IV: COMPUTER APPLICATIONS FOR THE FOOD SERVICE INDUSTRY. Chapter 15: Computer Applications for the Food Service Industry. A. Point of Sale Technology. B. Inventory Purchasing Software. C. Menu Printing. D. Case Study Step IX.

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Book SynopsisA comprehensive, must-have handbook of matrix methods with a unique emphasis on statistical applications This timely book, A Matrix Handbook for Statisticians, provides a comprehensive, encyclopedic treatment of matrices as they relate to both statistical concepts and methodologies.Trade Review"This book maintains its uniqueness among the competition through its extensive referencing to proofs and comprehensive coverage of topics not found in any other one book." (International Statistical Review, December 2008) "This book maintains its uniqueness among the competition through its extensive referencing to proofs and comprehensive coverage of topics not found in any other one book." (International Statistical Review, Dec 2008) "This is an authoritative and comprehensive reference that will be useful to researchers who need to use the results of matrix analysis in their work. It would also be a useful addition to the reference collection of any mathematical library." (MAA Review, March 2008)Table of ContentsPreface. 1. Notation. 1.1 General Definitions. 1.2 Some Continuous Univariate Distributions. 1.3 Glossary of Notation. 2. Vectors, Vector Spaces, and Convexity. 2.1 Vector Spaces. 2.1.1 Definitions. 2.1.2 Quadratic Subspaces. 2.1.3 Sums and Intersections of Subspaces. 2.1.4 Span and Basis. 2.1.5 Isomorphism. 2.2 Inner Products. 2.2.1 Definition and Properties. 2.2.2 Functionals. 2.2.3 Orthogonality. 2.2.4 Column and Null Spaces. 2.3 Projections. 2.3.1 General Projections. 2.3.2 Orthogonal Projections. 2.4 Metric Spaces. 2.5 Convex Sets and Functions. 2.6 Coordinate Geometry. 2.6.1 Hyperplanes and Lines. 2.6.2 Quadratics. 2.6.3 Miscellaneous Results. 3. Rank. 3.1 Some General Properties. 3.2 Matrix Products. 3.3 Matrix Cancellation Rules. 3.4 Matrix Sums. 3.5 Matrix Differences. 3.6 Partitioned Matrices. 3.7 Maximal and Minimal Ranks. 3.8 Matrix Index. 4. Matrix Functions: Inverse, Transpose, Trace, Determinant, and Norm. 4.1 Inverse. 4.2 Transpose. 4.3 Trace. 4.4 Determinants. 4.4.1 Introduction. 4.4.2 Adjoint Matrix. 4.4.3 Compound Matrix. 4.4.4 Expansion of a Determinant. 4.5 Permanents. 4.6 Norms. 4.6.1 Vector Norms. 4.6.2 Matrix Norms. 4.6.3 Unitarily Invariant Norms. 4.6.4 M,N-Invariant Norms. 4.6.5 Computational Accuracy. 5. Complex, Hermitian, and Related Matrices. 5.1 Complex Matrices. 5.1.1 Some General Results. 5.1.2 Determinants. 5.2 Hermitian Matrices. 5.3 Skew-Hermitian Matrices. 5.4 Complex Symmetric Matrices. 5.5 Real Skew-Symmetric Matrices. 5.6 Normal Matrices. 5.7 Quaternions. 6. Eigenvalues, Eigenvectors, and Singular Values. 6.1 Introduction and Definitions. 6.1.1 Characteristic Polynomial. 6.1.2 Eigenvalues. 6.1.3 Singular Values. 6.1.4 Functions of a Matrix. 6.1.5 Eigenvectors. 6.1.6 Hermitian Matrices. 6.1.7 Computational Methods. 6.1.8 Generalized Eigenvalues. 6.1.9 Matrix Products 103. 6.2 Variational Characteristics for Hermitian Matrices. 6.3 Separation Theorems. 6.4 Inequalities for Matrix Sums. 6.5 Inequalities for Matrix Differences. 6.6 Inequalities for Matrix Products. 6.7 Antieigenvalues and Antieigenvectors. 7. Generalized Inverses. 7.1 Definitions. 7.2 Weak Inverses. 7.2.1 General Properties. 7.2.2 Products. 7.2.3 Sums and Differences. 7.2.4 Real Symmetric Matrices. 7.2.5 Decomposition Methods. 7.3 Other Inverses. 7.3.1 Reflexive (g12) Inverse. 7.3.2 Minimum Norm (g14) Inverse. 7.3.3 Minimum Norm Reflexive (g124) Inverse. 7.3.4 Least Squares (g13) Inverse. 7.3.5 Least Squares Reflexive (g123) Inverse. 7.4 Moore-Penrose (g1234) Inverse. 7.4.1 General Properties. 7.4.2 Sums. 7.4.3 Products. 7.5 Group Inverse. 7.6 Some General Properties of Inverses. 8. Some Special Matrices. 8.1 Orthogonal and Unitary Matrices. 8.2 Permutation Matrices. 8.3 Circulant, Toeplitz, and Related Matrices. 8.3.1 Regular Circulant. 8.3.2 Symmetric Regular Circulant. 8.3.3 Symmetric Circulant. 8.3.4 Toeplitz Matrix. 8.3.5 Persymmetric Matrix. 8.3.6 Cross-Symmetric (Centrosymmetric) Matrix. 8.3.7 Block Circulant. 8.3.8 Hankel Matrix. 8.4 Diagonally Dominant Matrices. 8.5 Hadamard Matrices. 8.6 Idempotent Matrices. 8.6.1 General Properties. 8.6.2 Sums of Idempotent Matrices and Extensions. 8.6.3 Products of Idempotent Matrices. 8.7 Tripotent Matrices. 8.8 Irreducible Matrices. 8.9 Triangular Matrices. 8.10 Hessenberg Matrices. 8.11 Tridiagonal Matrices. 8.12 Vandermonde and Fourier Matrices. 8.12.1 Vandermonde Matrix. 8.12.2 Fourier Matrix. 8.13 Zero-One (0,1) Matrices. 8.14 Some Miscellaneous Matrices and Arrays. 8.14.1 Krylov Matrix. 8.14.2 Nilpotent and Unipotent Matrices. 8.14.3 Payoff Matrix. 8.14.4 Stable and Positive Stable Matrices. 8.14.5 P-Matrix. 8.14.6 Z- and M-Matrices. 8.14.7 Three-Dimensional Arrays. 9. Non-Negative Vectors and Matrices. 9.1 Introduction. 9.1.1 Scaling. 9.1.2 Modulus of a Matrix. 9.2 Spectral Radius. 9.2.1 General Properties. 9.2.2 Dominant Eigenvalue. 9.3 Canonical Form of a Non-negative Matrix. 9.4 Irreducible Matrices. 9.4.1 Irreducible Non-negative Matrix. 9.4.2 Periodicity. 9.4.3 Non-negative and Non-positive Off-Diagonal Elements. 9.4.4 Perron Matrix. 9.4.5 Decomposable Matrix. 9.5 Leslie Matrix. 9.6 Stochastic Matrices. 9.6.1 Basic Properties. 9.6.2 Finite Homogeneous Markov Chain. 9.6.3 Countably Infinite Stochastic Matrix. 9.6.4 Infinite Irreducible Stochastic Matrix. 9.7 Doubly Stochastic Matrices. 10. Positive Definite and Non-negative Definite Matrices. 10.1 Introduction. 10.2 Non-negative Definite Matrices. 10.2.1 Some General Properties. 10.2.2 Gram Matrix. 10.2.3 Doubly Non-negative Matrix. 10.3 Positive Definite Matrices. 10.4 Pairs of Matrices. 10.4.1 Non-Negative or Positive Definite Difference. 10.4.2 One or More Non-Negative Definite Matrices. 11. Special Products and Operators. 11.1 Kronecker Product. 11.1.1 Two Matrices. 11.1.2 More Than Two Matrices. 11.2 Vec Operator. 11.3 Vec-Permutation (Commutation) Matrix. 11.4 Generalized Vec-Permutation Matrix. 11.5 Vech Operator. 11.5.1 Symmetric Matrix. 11.5.2 Lower Triangular Matrix. 11.6 Star Operator. 11.7 Hadamard Product. 11.8 Rao-Khatri Product. 12. Inequalities. 12.1 Cauchy-Schwarz inequalities. 12.1.1 Real Vector Inequalities and Extensions. 12.1.2 Complex Vector Inequalities. 12.1.3 Real Matrix Inequalities. 12.1.4 Complex Matrix Inequalities. 12.2 H?older?s Inequality and Extensions. 12.3 Minkowski?s Inequality and Extensions. 12.4 Weighted Means. 12.5 Quasilinearization (Representation Theorems). 12.6 Some Geometrical Properties. 12.7 Miscellaneous Inequalities. 12.7.1 Determinants. 12.7.2 Trace. 12.7.3 Quadratics. 12.7.4 Sums and Products. 12.8 Some Identities. 13. Linear Equations. 13.1 Unknown vector. 13.1.1 Consistency. 13.1.2 Solutions. 13.1.3 Homogeneous Equations. 13.1.4 Restricted Equations. 13.2 Unknown Matrix. 13.2.1 Consistency. 13.2.2 Some Special Cases. 14. Partitioned Matrices. 14.1 Schur Complement. 14.2 Inverses. 14.3 Determinants. 14.4 Positive and Non-Negative Definite matrices. 14.5 Eigenvalues. 14.6 Generalized Inverses. 14.6.1 Weak Inverses. 14.6.2 Moore-Penrose Inverses. 14.7 Miscellaneous partitions. 15. Patterned Matrices. 15.1 Inverses. 15.2 Determinants. 15.3 Perturbations. 15.4 Matrices With Repeated Elements and Blocks. 15.5 Generalized Inverses. 15.5.1 Weak Inverses. 15.5.2 Moore-Penrose Inverses. 16. Factorization of Matrices. 16.1 Similarity Reductions. 16.2 Reduction by Elementary Transformations. 16.2.1 Types of Transformation. 16.2.2 Equivalence Relation. 16.2.3 Echelon Form. 16.2.4 Hermite Form. 16.3 Singular Value Decomposition (SVD). 16.4 Triangular Factorizations. 16.5 Orthogonal-Triangular Reductions. 16.6 Further Diagonal or Tridiagonal Reductions. 16.7 Congruence. 16.8 Simultaneous Reductions. 16.9 Polar Decomposition. 16.10 Miscellaneous Factorizations. 17. Differentiation and Finite Differences. 17.1 Introduction. 17.2 Scalar Differentiation. 17.2.1 Differentiation with Respect to t. 17.2.2 Differentiation With Respect to a Vector Element. 17.2.3 Differentiation With Respect to a Matrix Element. 17.3 Vector Differentiation: Scalar Function. 17.3.1 Basic Results. 17.3.2 x=vec X. 17.3.3 Function of a Function. 17.4 Vector Differentiation: Vector Function. 17.5 Matrix Differentiation: Scalar Function. 17.5.1 General Results. 17.5.2 f = trace. 17.5.3 f = determinant. 17.5.4 f = yrs. 17.5.5 f = eigenvalue. 17.6 Transformation Rules. 17.7 Matrix Differentiation: Matrix Function. 17.8 Matrix Differentials. 17.9 Perturbation Using Differentials. 17.10 Matrix Linear Differential Equations. 17.11 Second Order Derivatives. 17.12 Vector Difference Equations. 18. Jacobians. 18.1 Introduction. 18.2 Method of Differentials. 18.3 Further Techniques. 18.3.1 Chain Rule. 18.3.2 Exterior (Wedge) Product of Differentials. 18.3.3 Induced Functional Equations. 18.3.4 Jacobians Involving Transposes. 18.3.5 Patterned Matrices and L-Structures. 18.4 Vector Transformations. 18.5 Jacobians for Complex Vectors and Matrices. 18.6 Matrices with Functionally Independent Elements. 18.7 Symmetric and Hermitian Matrices. 18.8 Skew-Symmetric and Skew-Hermitian Matrices. 18.9 Triangular Matrices. 18.9.1 Linear Transformations. 18.9.2 Nonlinear Transformations of X. 18.9.3 Decompositions With One matrix Skew Symmetric. 18.9.4 Symmetric Y. 18.9.5 Positive Definite Y. 18.9.6 Hermitian Positive Definite Y. 18.9.7 Skew Symmetric Y. 18.9.8 LU Decomposition. 18.10 Decompositions Involving Diagonal Matrices. 18.10.1 Square Matrices. 18.10.2 One Triangular Matrix. 18.10.3 Symmetric and Skew Symmetric Matrices. 18.11 Positive?Definite Matrices. 18.12 Caley Transformation. 18.13 Diagonalizable Matrices. 18.14 Pairs of Matrices. 19. Matrix Limits, Sequences and Series. 19.1 Limits. 19.2 Sequences. 19.3 Asymptotically Equivalent Sequences. 19.4 Series. 19.5 Matrix Functions. 19.6 Matrix Exponentials. 20. Random Vectors. 20.1 Notation. 20.2 Variances and Covariances. 20.3 Correlations. 20.3.1 Population Correlations. 20.3.2 Sample Correlations. 20.4 Quadratics. 20.5 Multivariate Normal Distribution. 20.5.1 Definition and Properties. 20.5.2 Quadratics in Normal Variables. 20.5.3 Quadratics and Chi-squared. 20.5.4 Independence and Quadratics. 20.5.5 Independence of Several Quadratics. 20.6 Complex Random Vectors. 20.7 Regression Models. 20.7.1 V is the Identity Matrix. 20.7.2 V is Positive Definite. 20.7.3 V is Non-negative Definite. 20.8 Other Multivariate Distributions. 20.8.1 Multivariate t-Distribution. 20.8.2 Elliptical and Spherical Distributions. 20.8.3 Dirichlet Distributions. 21. Random Matrices. 21.1 Introduction. 21.2 Generalized Quadratic Forms. 21.2.1 General Results. 21.2.2 Wishart Distribution. 21.3 Random Samples. 21.3.1 One Sample. 21.3.2 Two Samples. 21.4 Multivariate Linear Model. 21.4.1 Least Squares Estimation. 21.4.2 Statistical Inference. 21.4.3 Two Extensions. 21.5 Dimension Reduction Techniques. 21.5.1 Principal Component Analysis (PCA). 21.5.2 Discriminant Coordinates. 21.5.3 Canonical Correlations and Variates. 21.5.4 Latent Variable Methods. 21.5.5 Classical (Metric) Scaling. 21.6 Procrustes Analysis (Matching Configurations). 21.7 Some Specific Random Matrices. 21.8 Allocation Problems. 21.9 Matrix Variate Distributions. 21.10 Matrix Ensembles. 22. Inequalities for Probabilities and Random Variables. 22.1 General Probabilities. 22.2 Bonferroni-Type Inequalities. 22.3 Distribution-Free Probability Inequalities. 22.3.1 Chebyshev-Type Inequalities. 22.3.2 Kolmogorov-Type Inequalities. 22.3.3 Quadratics and Inequalities. 22.4 Data Inequalities. 22.5 Inequalities for Expectations. 22.6 Multivariate Inequalities. 22.6.1 Convex Subsets. 22.6.2 Multivariate Normal. 22.6.3 Inequalities For Other Distributions. 23. Majorization. 23.1 General Properties. 23.2 Schur Convexity. 23.3 Probabilities and Random variables. 24. Optimization and Matrix Approximation. 24.1 Stationary Values. 24.2 Using Convex and Concave Functions. 24.3 Two General Methods. 24.3.1 Maximum Likelihood. 24.3.2 Least Squares. 24.4 Optimizing a Function of a Matrix. 24.4.1 Trace. 24.4.2 Norm. 24.4.3 Quadratics. 24.5 Optimal Designs. References. Index.

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Book SynopsisA complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experimentswith orderedtreatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinTable of ContentsPreface xv PART I BASIC EXPERIMENTAL DESIGN AND ANALYSIS 1 Review of Basic Statistical Methods 3 1.1 Introduction, 3 1.2 Elementary Statistical Inference, 4 1.3 Elementary Statistical Decision Theory, 7 1.4 Effect Size, 10 1.5 Measures of Association, 14 1.6 A Practical Alternative to Effect Sizes and Measures of Association That Is Relevant to the Individual: p(YTx > YControl), 17 1.7 Generalization of Results, 19 1.8 Control of Nuisance Variation, 20 1.9 Software, 22 1.10 Summary, 24 2 Review of Simple Correlated Samples Designs and Associated Analyses 25 2.1 Introduction, 25 2.2 Two-Level Correlated Samples Designs, 25 2.3 Software, 32 2.4 Summary, 32 3 ANOVA Basics for One-Factor Randomized Group, Randomized Block, and Repeated Measurement Designs 35 3.1 Introduction, 35 3.2 One-Factor Randomized Group Design and Analysis, 35 3.3 One-Factor Randomized Block Design and Analysis, 51 3.4 One-Factor Repeated Measurement Design and Analysis, 56 3.5 Summary, 60 PART II ESSENTIALS OF REGRESSION ANALYSIS 4 Simple Linear Regression 63 4.1 Introduction, 63 4.2 Comparison of Simple Regression and ANOVA, 63 4.3 Regression Estimation, Inference, and Interpretation, 68 4.4 Diagnostic Methods: Is the Model Apt?, 80 4.5 Summary, 82 5 Essentials of Multiple Linear Regression 85 5.1 Introduction, 85 5.2 Multiple Regression: Two-Predictor Case, 86 5.3 General Multiple Linear Regression: m Predictors, 105 5.4 Alternatives to OLS Regression, 115 5.5 Summary, 119 PART III ESSENTIALS OF SIMPLE AND MULTIPLE ANCOVA 6 One-Factor Analysis of Covariance 123 6.1 Introduction, 123 6.2 Analysis of Covariance Model, 127 6.3 Computation and Rationale, 128 6.4 Adjusted Means, 133 6.5 ANCOVA Example 1: Training Effects, 140 6.6 Testing Homogeneity of Regression Slopes, 144 6.7 ANCOVA Example 2: Sexual Activity Reduces Lifespan, 148 6.8 Software, 150 6.9 Summary, 157 7 Analysis of Covariance Through Linear Regression 159 7.1 Introduction, 159 7.2 Simple Analysis of Variance Through Linear Regression, 159 7.3 Analysis of Covariance Through Linear Regression, 172 7.4 Computation of Adjusted Means, 177 7.5 Similarity of ANCOVA to Part and Partial Correlation Methods, 177 7.6 Homogeneity of Regression Test Through General Linear Regression, 178 7.7 Summary, 179 8 Assumptions and Design Considerations 181 8.1 Introduction, 181 8.2 Statistical Assumptions, 182 8.3 Design and Data Issues Related to the Interpretation of ANCOVA, 200 8.4 Summary, 213 9 Multiple Comparison Tests and Confidence Intervals 215 9.1 Introduction, 215 9.2 Overview of Four Multiple Comparison Procedures, 215 9.3 Tests on All Pairwise Comparisons: Fisher–Hayter, 216 9.4 All Pairwise Simultaneous Confidence Intervals and Tests: Tukey–Kramer, 219 9.5 Planned Pairwise and Complex Comparisons: Bonferroni, 222 9.6 Any or All Comparisons: Scheff´e, 225 9.7 Ignore Multiple Comparison Procedures?, 227 9.8 Summary, 228 10 Multiple Covariance Analysis 229 10.1 Introduction, 229 10.2 Multiple ANCOVA Through Multiple Regression, 232 10.3 Testing Homogeneity of Regression Planes, 234 10.4 Computation of Adjusted Means, 236 10.5 Multiple Comparison Procedures for Multiple ANCOVA, 237 10.6 Software: Multiple ANCOVA and Associated Tukey–Kramer Multiple Comparison Tests Using Minitab, 243 10.7 Summary, 246 PART IV ALTERNATIVES FOR ASSUMPTION DEPARTURES 11 Johnson–Neyman and Picked-Points Solutions for Heterogeneous Regression 249 11.1 Introduction, 249 11.2 J–N and PPA Methods for Two Groups, One Covariate, 251 11.3 A Common Method That Should Be Avoided, 269 11.4 Assumptions, 270 11.5 Two Groups, Multiple Covariates, 272 11.6 Multiple Groups, One Covariate, 277 11.7 Any Number of Groups, Any Number of Covariates, 278 11.8 Two-Factor Designs, 278 11.9 Interpretation Problems, 279 11.10 Multiple Dependent Variables, 281 11.11 Nonlinear Johnson-Neyman Analysis, 282 11.12 Correlated Samples, 282 11.13 Robust Methods, 282 11.14 Software, 283 11.15 Summary, 283 12 Nonlinear ANCOVA 285 12.1 Introduction, 285 12.2 Dealing with Nonlinearity, 286 12.3 Computation and Example of Fitting Polynomial Models, 288 12.4 Summary, 295 13 Quasi-ANCOVA: When Treatments Affect Covariates 297 13.1 Introduction, 297 13.2 Quasi-ANCOVA Model, 298 13.3 Computational Example of Quasi-ANCOVA, 300 13.4 Multiple Quasi-ANCOVA, 304 13.5 Computational Example of Multiple Quasi-ANCOVA, 304 13.6 Summary, 308 14 Robust ANCOVA/Robust Picked Points 311 14.1 Introduction, 311 14.2 Rank ANCOVA, 311 14.3 Robust General Linear Model, 314 14.4 Summary, 320 15 ANCOVA for Dichotomous Dependent Variables 321 15.1 Introduction, 321 15.2 Logistic Regression, 323 15.3 Logistic Model, 324 15.4 Dichotomous ANCOVA Through Logistic Regression, 325 15.5 Homogeneity of Within-Group Logistic Regression, 328 15.6 Multiple Covariates, 328 15.7 Multiple Comparison Tests, 330 15.8 Continuous Versus Forced Dichotomy Results, 331 15.9 Summary, 331 16 Designs with Ordered Treatments and No Covariates 333 16.1 Introduction, 333 16.2 Qualitative, Quantitative, and Ordered Treatment Levels, 333 16.3 Parametric Monotone Analysis, 337 16.4 Nonparametric Monotone Analysis, 346 16.5 Reversed Ordinal Logistic Regression, 350 16.6 Summary, 353 17 ANCOVA for Ordered Treatments Designs 355 17.1 Introduction, 355 17.2 Generalization of the Abelson–Tukey Method to Include One Covariate, 355 17.3 Abelson–Tukey: Multiple Covariates, 358 17.4 Rank-Based ANCOVA Monotone Method, 359 17.5 Rank-Based Monotone Method with Multiple Covariates, 362 17.6 Reversed Ordinal Logistic Regression with One or More Covariates, 362 17.7 Robust R-Estimate ANCOVA Monotone Method, 363 17.8 Summary, 364 PART V SINGLE-CASE DESIGNS 18 Simple Interrupted Time-Series Designs 367 18.1 Introduction, 367 18.2 Logic of the Two-Phase Design, 370 18.3 Analysis of the Two-Phase (AB) Design, 371 18.4 Two Strategies for Time-Series Regression Intervention Analysis, 374 18.5 Details of Strategy II, 375 18.6 Effect Sizes, 385 18.7 Sample Size Recommendations, 389 18.8 When the Model Is Too Simple, 393 18.9 Summary, 394 19 Examples of Single-Case AB Analysis 403 19.1 Introduction, 403 19.2 Example I: Cancer Death Rates in the United Kingdom, 403 19.3 Example II: Functional Activity, 411 19.4 Example III: Cereal Sales, 414 19.5 Example IV: Paracetamol Poisoning, 424 19.6 Summary, 430 20 Analysis of Single-Case Reversal Designs 433 20.1 Introduction, 433 20.2 Statistical Analysis of Reversal Designs, 434 20.3 Computational Example: Pharmacy Wait Time, 441 20.4 Summary, 452 21 Analysis of Multiple-Baseline Designs 453 21.1 Introduction, 453 21.2 Case I Analysis: Independence of Errors Within and Between Series, 455 21.3 Case II Analysis: Autocorrelated Errors Within Series, Independence Between Series, 461 21.4 Case III Analysis: Independent Errors Within Series, Cross-Correlation Between Series, 461 21.5 Intervention Versus Control Series Design, 467 21.6 Summary, 471 PART VI ANCOVA EXTENSIONS 22 Power Estimation 475 22.1 Introduction, 475 22.2 Power Estimation for One-Factor ANOVA, 475 22.3 Power Estimation for ANCOVA, 480 22.4 Power Estimation for Standardized Effect Sizes, 482 22.5 Summary, 482 23 ANCOVA for Randomized-Block Designs 483 23.1 Introduction, 483 23.2 Conventional Design and Analysis Example, 484 23.3 Combined Analysis (ANCOVA and Blocking Factor), 486 23.4 Summary, 488 24 Two-Factor Designs 489 24.1 Introduction, 489 24.2 ANCOVA Model and Computation for Two-Factor Designs, 494 24.3 Multiple Comparison Tests for Adjusted Marginal Means, 512 24.4 Two-Factor ANOVA and ANCOVA for Repeated-Measurement Designs, 519 24.5 Summary, 530 25 Randomized Pretest–Posttest Designs 531 25.1 Introduction, 531 25.2 Comparison of Three ANOVA Methods, 531 25.3 ANCOVA for Pretest–Posttest Designs, 534 25.4 Summary, 539 26 Multiple Dependent Variables 541 26.1 Introduction, 541 26.2 Uncorrected Univariate ANCOVA, 543 26.3 Bonferroni Method, 544 26.4 Multivariate Analysis of Covariance (MANCOVA), 544 26.5 MANCOVA Through Multiple Regression Analysis: Two Groups Only, 553 26.6 Issues Associated with Bonferroni F and MANCOVA, 554 26.7 Alternatives to Bonferroni and MANCOVA, 555 26.8 Example Analyses Using Minitab, 557 26.9 Summary, 564 PART VII QUASI-EXPERIMENTS AND MISCONCEPTIONS 27 Nonrandomized Studies: Measurement Error Correction 567 27.1 Introduction, 567 27.2 Effects of Measurement Error: Randomized-Group Case, 568 27.3 Effects of Measurement Error in Exposure and Covariates: Nonrandomized Design, 569 27.4 Measurement Error Correction Ideas, 570 27.5 Summary, 573 28 Design and Analysis of Observational Studies 575 28.1 Introduction, 575 28.2 Design of Nonequivalent Group/Observational Studies, 579 28.3 Final (Outcome) Analysis, 587 28.4 Propensity Design Advantages, 592 28.5 Evaluations of ANCOVA Versus Propensity-Based Approaches, 594 28.6 Adequacy of Observational Studies, 596 28.7 Summary, 597 29 Common ANCOVA Misconceptions 599 29.1 Introduction, 599 29.2 SSAT Versus SSIntuitive AT: Single Covariate Case, 599 29.3 SSAT Versus SSIntuitive AT: Multiple Covariate Case, 601 29.4 ANCOVA Versus ANOVA on Residuals, 606 29.5 ANCOVA Versus Y/X Ratio, 606 29.6 Other Common Misconceptions, 607 29.7 Summary, 608 30 Uncontrolled Clinical Trials 609 30.1 Introduction, 609 30.2 Internal Validity Threats Other Than Regression, 610 30.3 Problems with Conventional Analyses, 613 30.4 Controlling Regression Effects, 615 30.5 Naranjo–Mckean Dual Effects Model, 616 30.6 Summary, 617 Appendix: Statistical Tables 619 References 643 Index 655

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Book SynopsisA indispensable guide to understanding and designing modern experiments The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences. The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphicaTable of ContentsPreface xv Abbreviations xxi 1 Introduction 1 1.1 Observational Studies and Experiments 1 1.2 Brief Historical Remarks 4 1.3 Basic Terminology and Concepts of Experimentation 5 1.4 Basic Principles of Experimentation 9 1.4.1 How to Minimize Biases and Variability? 9 1.4.2 Sequential Experimentation 14 1.5 Chapter Summary 15 Exercises 16 2 Review of Elementary Statistics 20 2.1 Experiments for a Single Treatment 20 2.1.1 Summary Statistics and Graphical Plots 21 2.1.2 Confidence Intervals and Hypothesis Tests 25 2.1.3 Power and Sample Size Calculation 27 2.2 Experiments for Comparing Two Treatments 28 2.2.1 Independent Samples Design 29 2.2.2 Matched Pairs Design 38 2.3 Linear Regression 41 2.3.1 Simple Linear Regression 42 2.3.2 Multiple Linear Regression 50 2.4 Chapter Summary 62 Exercises 62 3 Single Factor Experiments: Completely Randomized Designs 70 3.1 Summary Statistics and Graphical Displays 71 3.2 Model 73 3.3 Statistical Analysis 75 3.3.1 Estimation 75 3.3.2 Analysis of Variance 76 3.3.3 Confidence Intervals and Hypothesis Tests 78 3.4 Model Diagnostics 79 3.4.1 Checking Homoscedasticity 80 3.4.2 Checking Normality 81 3.4.3 Checking Independence 81 3.4.4 Checking Outliers 81 3.5 Data Transformations 85 3.6 Power of F -Test and Sample Size Determination 87 3.7 Quantitative Treatment Factors 90 3.8 One-Way Analysis of Covariance 96 3.8.1 Randomized Block Design versus Analysis of Covariance 96 3.8.2 Model 96 3.8.3 Statistical Analysis 98 3.9 Chapter Notes 106 3.9.1 Randomization Distribution of F -Statistic 106 3.9.2 F -Test for Heteroscedastic Treatment Variances 108 3.9.3 Derivations of Formulas for Orthogonal Polynomials 110 3.9.4 Derivation of LS Estimators for One-Way Analysis of Covariance 112 3.10 Chapter Summary 113 Exercises 114 4 Single-Factor Experiments: Multiple Comparison and Selection Procedures 126 4.1 Basic Concepts of Multiple Comparisons 127 4.1.1 Family 127 4.1.2 Familywise Error Rate 128 4.1.3 Bonferroni Method 129 4.1.4 Union–Intersection Method 130 4.1.5 Closure Method 131 4.2 Pairwise Comparisons 132 4.2.1 Least Significant Difference and Bonferroni Procedures 133 4.2.2 Tukey Procedure for Pairwise Comparisons 134 4.2.3 Step-Down Procedures for Pairwise Comparisons 136 4.3 Comparisons with a Control 139 4.3.1 Dunnett Procedure for Comparisons with a Control 139 4.3.2 Step-Down Procedures for Comparisons with a Control 142 4.4 General Contrasts 144 4.4.1 Tukey Procedure for Orthogonal Contrasts 145 4.4.2 Scheffé Procedure for All Contrasts 146 4.5 Ranking and Selection Procedures 148 4.5.1 Indifference-Zone Formulation 148 4.5.2 Subset Selection Formulation 154 4.5.3 Multiple Comparisons with the Best 155 4.5.4 Connection between Multiple Comparisons with Best and Selection of Best Treatment 157 4.6 Chapter Summary 158 Exercises 159 5 Randomized Block Designs and Extensions 168 5.1 Randomized Block Designs 169 5.1.1 Model 169 5.1.2 Statistical Analysis 171 5.1.3 Randomized Block Designs with Replicates 177 5.2 Balanced Incomplete Block Designs 180 5.2.1 Statistical Analysis 182 5.2.2 Interblock Analysis 185 5.3 Youden Square Designs 188 5.3.1 Statistical Analysis 189 5.4 Latin Square Designs 192 5.4.1 Choosing a Latin Square 192 5.4.2 Model 195 5.4.3 Statistical Analysis 195 5.4.4 Crossover Designs 198 5.4.5 Graeco–Latin Square Designs 202 5.5 Chapter Notes 205 5.5.1 Restriction Error Model for Randomized Block Designs 205 5.5.2 Derivations of Formulas for BIB Design 206 5.6 Chapter Summary 211 Exercises 212 6 General Factorial Experiments 224 6.1 Factorial versus One-Factor-at-a-Time Experiments 225 6.2 Balanced Two-Way Layouts 227 6.2.1 Summary Statistics and Graphical Plots 227 6.2.2 Model 230 6.2.3 Statistical Analysis 231 6.2.4 Model Diagnostics 235 6.2.5 Tukey’s Test for Interaction for Singly Replicated Two-Way Layouts 236 6.3 Unbalanced Two-Way Layouts 240 6.3.1 Statistical Analysis 240 6.4 Chapter Notes 245 6.4.1 Derivation of LS Estimators of Parameters for Balanced Two-Way Layouts 245 6.4.2 Derivation of ANOVA Sums of Squares and F -Tests for Balanced Two-Way Layouts 246 6.4.3 Three- and Higher Way Layouts 248 6.5 Chapter Summary 250 Exercises 250 7 Two-Level Factorial Experiments 256 7.1 Estimation of Main Effects and Interactions 257 7.1.1 22 Designs 257 7.1.2 23 Designs 261 7.1.3 2p Designs 266 7.2 Statistical Analysis 267 7.2.1 Confidence Intervals and Hypothesis Tests 267 7.2.2 Analysis of Variance 268 7.2.3 Model Fitting and Diagnostics 270 7.3 Single-Replicate Case 272 7.3.1 Normal and Half-Normal Plots of Estimated Effects 272 7.3.2 Lenth Method 278 7.3.3 Augmenting a 2p Design with Observations at the Center Point 279 7.4 2p Factorial Designs in Incomplete Blocks: Confounding of Effects 282 7.4.1 Construction of Designs 282 7.4.2 Statistical Analysis 286 7.5 Chapter Notes 287 7.5.1 Yates Algorithm 287 7.5.2 Partial Confounding 288 7.6 Chapter Summary 289 Exercises 290 8 Two-Level Fractional Factorial Experiments 300 8.1 2p−q Fractional Factorial Designs 301 8.1.1 2p−1 Fractional Factorial Design 301 8.1.2 General 2p−q Fractional Factorial Designs 307 8.1.3 Statistical Analysis 312 8.1.4 Minimum Aberration Designs 316 8.2 Plackett–Burman Designs 317 8.3 Hadamard Designs 323 8.4 Supersaturated Designs 325 8.4.1 Construction of Supersaturated Designs 325 8.4.2 Statistical Analysis 327 8.5 Orthogonal Arrays 329 8.6 Sequential Assemblies of Fractional Factorials 333 8.6.1 Foldover of Resolution III Designs 334 8.6.2 Foldover of Resolution IV Designs 337 8.7 Chapter Summary 338 Exercises 339 9 Three-Level and Mixed-Level Factorial Experiments 351 9.1 Three-Level Full Factorial Designs 351 9.1.1 Linear–Quadratic System 353 9.1.2 Orthogonal Component System 361 9.2 Three-Level Fractional Factorial Designs 364 9.3 Mixed-Level Factorial Designs 372 9.3.1 2p4q Designs 373 9.3.2 2p3q Designs 378 9.4 Chapter Notes 386 9.4.1 Alternative Derivations of Estimators of Linear and Quadratic Effects 386 9.5 Chapter Summary 388 Exercises 389 10 Experiments for Response Optimization 395 10.1 Response Surface Methodology 396 10.1.1 Outline of Response Surface Methodology 396 10.1.2 First-Order Experimentation Phase 397 10.1.3 Second-Order Experimentation Phase 402 10.2 Mixture Experiments 412 10.2.1 Designs for Mixture Experiments 414 10.2.2 Analysis of Mixture Experiments 416 10.3 Taguchi Method of Quality Improvement 419 10.3.1 Philosophy Underlying Taguchi Method 422 10.3.2 Implementation of Taguchi Method 425 10.3.3 Critique of Taguchi Method 432 10.4 Chapter Summary 436 Exercises 437 11 Random and Mixed Crossed-Factors Experiments 448 11.1 One-Way Layouts 449 11.1.1 Random-Effects Model 449 11.1.2 Analysis of Variance 450 11.1.3 Estimation of Variance Components 452 11.2 Two-Way Layouts 455 11.2.1 Random-Effects Model 455 11.2.2 Mixed-Effects Model 459 11.3 Three-Way Layouts 464 11.3.1 Random- and Mixed-Effects Models 464 11.3.2 Analysis of Variance 465 11.3.3 Approximate F -Tests 468 11.4 Chapter Notes 472 11.4.1 Maximum Likelihood and Restricted Maximum Likelihood (REML) Estimation of Variance Components 472 11.4.2 Derivations of Results for One- and Two-Way Random-Effects Designs 475 11.4.3 Relationship between Unrestricted and Restricted Models 478 11.5 Chapter Summary 479 Exercises 480 12 Nested, Crossed–Nested, and Split-Plot Experiments 487 12.1 Two-Stage Nested Designs 488 12.1.1 Model 488 12.1.2 Analysis of Variance 489 12.2 Three-Stage Nested Designs 490 12.2.1 Model 491 12.2.2 Analysis of Variance 492 12.3 Crossed and Nested Designs 495 12.3.1 Model 495 12.3.2 Analysis of Variance 496 12.4 Split-Plot Designs 501 12.4.1 Model 504 12.4.2 Analysis of Variance 505 12.4.3 Extensions of Split-Plot Designs 508 12.5 Chapter Notes 515 12.5.1 Derivations of E(MS) Expressions for Two-Stage Nested Design of Section 12.1 with Both Factors Random 515 12.5.2 Derivations of E(MS) Expressions for Design of Section 12.3 with Crossed and Nested Factors 517 12.5.3 Derivations of E(MS) Expressions for Split-Plot Design 520 12.6 Chapter Summary 523 Exercises 524 13 Repeated Measures Experiments 536 13.1 Univariate Approach 536 13.1.1 Model 537 13.1.2 Univariate Analysis of Variance for RM Designs 537 13.2 Multivariate Approach 548 13.2.1 One-Way Multivariate Analysis of Variance 548 13.2.2 Multivariate Analysis of Variance for RM Designs 549 13.3 Chapter Notes 555 13.3.1 Derivations of E(MS) Expressions for Repeated Measures Design Assuming Compound Symmetry 555 13.4 Chapter Summary 558 Exercises 559 14 Theory of Linear Models with Fixed Effects 566 14.1 Basic Linear Model and Least Squares Estimation 566 14.1.1 Geometric Interpretation of Least Squares Estimation 568 14.1.2 Least Squares Estimation in Singular Case 570 14.1.3 Least Squares Estimation in Orthogonal Case 572 14.2 Confidence Intervals and Hypothesis Tests 573 14.2.1 Sampling Distribution of ̂β 573 14.2.2 Sampling Distribution of s2 574 14.2.3 Inferences on Scalar Parameters 575 14.2.4 Inferences on Vector Parameters 575 14.2.5 Extra Sum of Squares Method 577 14.2.6 Analysis of Variance 579 14.3 Power of F-Test 583 14.4 Chapter Notes 586 14.4.1 Proof of Theorem 14.1 (Gauss–Markov Theorem) 586 14.4.2 Proof of Theorem 14.2 586 14.5 Chapter Summary 587 Exercises 588 Appendix A Vector-Valued Random Variables and Some Distribution Theory 595 A.1 Mean Vector and Covariance Matrix of Random Vector 596 A.2 Covariance Matrix of Linear Transformation of Random Vector 597 A.3 Multivariate Normal Distribution 598 A.4 Chi-Square, F-, and t-Distributions 599 A.5 Distributions of Quadratic Forms 601 A.6 Multivariate t-Distribution 605 A.7 Multivariate Normal Sampling Distribution Theory 606 Appendix B Case Studies 608 B.1 Case Study 1: Effects of Field Strength and Flip Angle on MRI Contrast 608 B.1.1 Introduction 608 B.1.2 Design 609 B.1.3 Data Analysis 610 B.1.4 Results 612 B.2 Case Study 2: Growing Stem Cells for Bone Implants 613 B.2.1 Introduction 613 B.1.2 Design 614 B.2.3 Data Analysis 614 B.2.4 Results 614 B.3 Case Study 3: Router Bit Experiment 619 B.3.1 Introduction 619 B.3.2 Design 619 B.3.3 Data Analysis 623 B.3.4 Results 624 Appendix C Statistical Tables 627 Answers to Selected Exercises 644 References 664 Index 675

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Book SynopsisThis new edition discusses classical linear models from a matrix algebra perspective, making the subject easily accessible to readers encountering linear models for the first time. It provides a solid foundation from which to explore the literature and interpret correctly the output of computer packages and brings together a number of approaches.Trade Review"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." (International Statistical Review, December 2008) "This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." (International Statistical Review, Dec 2008) "This well-written book represents various topics on linear models with great clarity in an easy-to-understand style." (CHOICE, Aug 2008)Table of ContentsPreface. 1. Introduction. 2. Matrix Algebra. 3. Random Vectors and Matrices. 4. Multivariate Normal Distribution. 5. Distribution of Quadratic Forms in y. 6. Simple Linear Regression. 7. Multiple Regression: Estimation. 8. Multiple Regression: tests of Hypotheses and Confidence Intervals. 9. Multiple Regression: Model Validation and Diagnostics. 10. Multiple Regression: random x's. 11. Multiple Regression: Bayesian Inference. 12. Analysis-of-Variance Models. 13. One-Way Analysis-of-Variance: balanced Case. 14. Two-Way Analysis-of Variance: Balanced Case. 15. Analysis-of-Variance: The Cell Means Model for Unbalanced Data. 16. Analysis-of-Covariance. 17. Linear Mixed Models. 18. Additional Models. Appendix A. Answers and Hits to the Problems. References. Index.

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Book SynopsisThis book provides statistical methods and models that can be used to produce short-term forecasts. The authors provide an intermediate-level discussion of a variety of statistical forecasting methods and models, to explain their interconnections, and to bridge the gap between theory and practice. .Table of Contents1. Introduction and Summary. 2. The Regression Model and Its Application in Forecasting. 3. Regression and Exponential Smoothing Methods to Forecast Nonseasonal Time Series. 4. Regression and Exponential Smoothing Methods to Forecast Seasonal Time Series. 5. Stochastic Time Series Models. 6. Seasonal Autoregressive Integrated Moving Average Models. 7. Relationships Between Forecasts from General Exponential Smoothing and Forecasts from Arima Time Series Models. 8. Special Topics. References. Exercises. Data Appendix. Table Appendix. Author Index. Subject Index.

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Book SynopsisThis book explores the martingale approach to the statistical analysis of counting processes, with an emphasis on application of those methods to censored failure time data. Introduced in the 1970s, this approach has proven to be remarkably successful in yielding results about statistical methods for many problems arising in censored data.Trade Review"…a unique source for combining the theory and the application of the survival analysis with censored data." (Technometrics, August 2007)Table of ContentsPreface. 0. The Applied Setting. 1. The Counting Process and Martingale Framework. 2. Local Square Integrable Martingales. 3. Finite Sample Moments and Large Sample Consistency of Tests and Estimators. 4. Censored Data Regression Models and Their Application. 5. Martingale Central Limit Theorem. 6. Large Sample results of the Kaplan-Meier Estimator. 7. Weighted Logrank Statistics. 8. Distribution Theory for Proportional Hazards Regression. Appendix A: Some Results from stieltjes Integration and Probability Theory. Appendix B: An Introduction to Weak convergence. Appendix C: The Martingale Central Limit Theorem: Some Preliminaries. Appendix D: Data. Appendix E: Exercises. Bibliography. Notation. Author Index. Subject Index.

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Book SynopsisMethods and guidelines for developing and using mathematical models Turn to Effective Groundwater Model Calibration for a set of methods and guidelines that can help produce more accurate and transparent mathematical models. The models can represent groundwater flow and transport and other natural and engineered systems.Trade Review"This is an excellent textbook that addresses a topic, optimization of multiparameter models, which is of broad interest." (Journal of American Water Resources Association, October 2007) "The book represents a very good combination of long-time expert knowledge and being up to date." (Clean, January 2008) "…a welcome addition to my collection of hydrogeologic books…a valuable reference for ground water scientists who use models." (Ground Water, January-February 2008)Table of ContentsPreface. 1 Introduction. 1.1 Book and Associated Contributions: Methods, Guidelines, Exercises, Answers, Software, and PowerPoint Files. 1.2 Model Calibration with Inverse Modeling. 1.2.1 Parameterization. 1.2.2 Objective Function. 1.2.3 Utility of Inverse Modeling and Associated Methods. 1.2.4 Using the Model to Quantitatively Connect Parameters, Observations, and Predictions. 1.3 Relation of this Book to Other Ideas and Previous Works. 1.3.1 Predictive Versus Calibrated Models. 1.3.2 Previous Work. 1.4 A Few Definitions. 1.4.1 Linear and Nonlinear. 1.4.2 Precision, Accuracy, Reliability, and Uncertainty. 1.5 Advantageous Expertise and Suggested Readings. 1.6 Overview of Chapters 2 Through 15. 2 Computer Software and Groundwater Management Problem Used in the Exercises. 2.1 Computer Programs MODFLOW-2000, UCODE_2005, and PEST. 2.2 Groundwater Management Problem Used for the Exercises. 2.2.1 Purpose and Strategy. 2.2.2 Flow System Characteristics. 2.3 Exercises. Exercise 2.1: Simulate Steady-State Heads and Perform Preparatory Steps. 3 Comparing Observed and Simulated Values Using Objective Functions. 3.1 Weighted Least-Squares Objective Function. 3.1.1 With a Diagonal Weight Matrix. 3.1.2 With a Full Weight Matrix. 3.2 Alternative Objective Functions. 3.2.1 Maximum-Likelihood Objective Function. 3.2.2 L1 Norm Objective Function. 3.2.3 Multiobjective Function. 3.3 Requirements for Accurate Simulated Results. 3.3.1 Accurate Model. 3.3.2 Unbiased Observations and Prior Information. 3.3.3 Weighting Reflects Errors. 3.4 Additional Issues. 3.4.1 Prior Information. 3.4.2 Weighting. 3.4.3 Residuals and Weighted Residuals. 3.5 Least-Squares Objective-Function Surfaces. 3.6 Exercises. Exercise 3.1: Steady-State Parameter Definition. Exercise 3.2: Observations for the Steady-State Problem. Exercise 3.3: Evaluate Model Fit Using Starting Parameter Values. 4 Determining the Information that Observations Provide on Parameter Values using Fit-Independent Statistics. 4.1 Using Observations. 4.1.1 Model Construction and Parameter Definition. 4.1.2 Parameter Values. 4.2 When to Determine the Information that Observations Provide About Parameter Values. 4.3 Fit-Independent Statistics for Sensitivity Analysis. 4.3.1 Sensitivities. 4.3.2 Scaling. 4.3.3 Dimensionless Scaled Sensitivities (dss). 4.3.4 Composite Scaled Sensitivities (css). 4.3.5 Parameter Correlation Coefficients (pcc). 4.3.6 Leverage Statistics. 4.3.7 One-Percent Scaled Sensitivities. 4.4 Advantages and Limitations of Fit-Independent Statistics for Sensitivity Analysis. 4.4.1 Scaled Sensitivities. 4.4.2 Parameter Correlation Coefficients. 4.4.3 Leverage Statistics. 4.5 Exercises. Exercise 4.1: Sensitivity Analysis for the Steady-State Model with Starting Parameter Values. 5 Estimating Parameter Values. 5.1 The Modified Gauss–Newton Gradient Method. 5.1.1 Normal Equations. 5.1.2 An Example. 5.1.3 Convergence Criteria. 5.2 Alternative Optimization Methods. 5.3 Multiobjective Optimization. 5.4 Log-Transformed Parameters. 5.5 Use of Limits on Estimated Parameter Values. 5.6 Exercises. Exercise 5.1: Modified Gauss–Newton Method and Application to a Two-Parameter Problem. Exercise 5.2: Estimate the Parameters of the Steady-State Model. 6 Evaluating Model Fit. 6.1 Magnitude of Residuals and Weighted Residuals. 6.2 Identify Systematic Misfit. 6.3 Measures of Overall Model Fit. 6.3.1 Objective-Function Value. 6.3.2 Calculated Error Variance and Standard Error. 6.3.3 AIC, AICc, and BIC Statistics. 6.4 Analyzing Model Fit Graphically and Related Statistics. 6.4.1 Using Graphical Analysis of Weighted Residuals to Detect Model Error. 6.4.2 Weighted Residuals Versus Weighted or Unweighted Simulated Values and Minimum, Maximum, and Average Weighted Residuals. 6.4.3 Weighted or Unweighted Observations Versus Simulated Values and Correlation Coefficient R. 6.4.4 Graphs and Maps Using Independent Variables and the Runs Statistic. 6.4.5 Normal Probability Graphs and Correlation Coefficient RN2. 6.4.6 Acceptable Deviations from Random, Normally Distributed Weighted Residuals. 6.5 Exercises. Exercise 6.1: Statistical Measures of Overall Fit. Exercise 6.2: Evaluate Graph Model fit and Related Statistics. 7 Evaluating Estimated Parameter Values and Parameter Uncertainty. 7.1 Reevaluating Composite Scaled Sensitivities. 7.2 Using Statistics from the Parameter Variance–Covariance Matrix. 7.2.1 Five Versions of the Variance–Covariance Matrix. 7.2.2 Parameter Variances, Covariances, Standard Deviations, Coefficients of Variation, and Correlation Coefficients. 7.2.3 Relation Between Sample and Regression Statistics. 7.2.4 Statistics for Log-Transformed Parameters. 7.2.5 When to Use the Five Versions of the Parameter Variance–Covariance Matrix. 7.2.6 Some Alternate Methods: Eigenvectors, Eigenvalues, and Singular Value Decomposition. 7.3 Identifying Observations Important to Estimated Parameter Values. 7.3.1 Leverage Statistics. 7.3.2 Influence Statistics. 7.4 Uniqueness and Optimality of the Estimated Parameter Values. 7.5 Quantifying Parameter Value Uncertainty. 7.5.1 Inferential Statistics. 7.5.2 Monte Carlo Methods. 7.6 Checking Parameter Estimates Against Reasonable Values. 7.7 Testing Linearity. 7.8 Exercises. Exercise 7.1: Parameter Statistics. Exercise 7.2: Consider All the Different Correlation Coefficients Presented. Exercise 7.3: Test for Linearity. 8 Evaluating Model Predictions, Data Needs, and Prediction Uncertainty. 8.1 Simulating Predictions and Prediction Sensitivities and Standard Deviations. 8.2 Using Predictions to Guide Collection of Data that Directly Characterize System Properties. 8.2.1 Prediction Scaled Sensitivities (pss). 8.2.2 Prediction Scaled Sensitivities Used in Conjunction with Composite Scaled Sensitivities. 8.2.3 Parameter Correlation Coefficients without and with Predictions. 8.2.4 Composite and Prediction Scaled Sensitivities Used with Parameter Correlation Coefficients. 8.2.5 Parameter–Prediction ( ppr) Statistic. 8.3 Using Predictions to Guide Collection of Observation Data. 8.3.1 Use of Prediction, Composite, and Dimensionless Scaled Sensitivities and Parameter Correlation Coefficients. 8.3.2 Observation–Prediction (opr) Statistic. 8.3.3 Insights About the opr Statistic from Other Fit-Independent Statistics. 8.3.4 Implications for Monitoring Network Design. 8.4 Quantifying Prediction Uncertainty Using Inferential Statistics. 8.4.1 Definitions. 8.4.2 Linear Confidence and Prediction Intervals on Predictions. 8.4.3 Nonlinear Confidence and Prediction Intervals. 8.4.4 Using the Theis Example to Understand Linear and Nonlinear Confidence Intervals. 8.4.5 Differences and Their Standard Deviations, Confidence Intervals, and Prediction Intervals. 8.4.6 Using Confidence Intervals to Serve the Purposes of Traditional Sensitivity Analysis. 8.5 Quantifying Prediction Uncertainty Using Monte Carlo Analysis. 8.5.1 Elements of a Monte Carlo Analysis. 8.5.2 Relation Between Monte Carlo Analysis and Linear and Nonlinear Confidence Intervals. 8.5.3 Using the Theis Example to Understand Monte Carlo Methods. 8.6 Quantifying Prediction Uncertainty Using Alternative Models. 8.7 Testing Model Nonlinearity with Respect to the Predictions. 8.8 Exercises. Exercise 8.1: Predict Advective Transport and Perform Sensitivity Analysis. Exercise 8.2: Prediction Uncertainty Measured Using Inferential Statistics. 9 Calibrating Transient and Transport Models and Recalibrating Existing Models. 9.1 Strategies for Calibrating Transient Models. 9.1.1 Initial Conditions. 9.1.2 Transient Observations. 9.1.3 Additional Model Inputs. 9.2 Strategies for Calibrating Transport Models. 9.2.1 Selecting Processes to Include. 9.2.2 Defining Source Geometry and Concentrations. 9.2.3 Scale Issues. 9.2.4 Numerical Issues: Model Accuracy and Execution Time. 9.2.5 Transport Observations. 9.2.6 Additional Model Inputs. 9.2.7 Examples of Obtaining a Tractable, Useful Model. 9.3 Strategies for Recalibrating Existing Models. 9.4 Exercises (optional). Exercises 9.1 and 9.2: Simulate Transient Hydraulic Heads and Perform Preparatory Steps. Exercise 9.3: Transient Parameter Definition. Exercise 9.4: Observations for the Transient Problem. Exercise 9.5: Evaluate Transient Model Fit Using Starting Parameter Values. Exercise 9.6: Sensitivity Analysis for the Initial Model. Exercise 9.7: Estimate Parameters for the Transient System by Nonlinear Regression. Exercise 9.8: Evaluate Measures of Model Fit. Exercise 9.9: Perform Graphical Analyses of Model Fit and Evaluate Related Statistics. Exercise 9.10: Evaluate Estimated Parameters. Exercise 9.11: Test for Linearity. Exercise 9.12: Predictions. 10 Guidelines for Effective Modeling. 10.1 Purpose of the Guidelines. 10.2 Relation to Previous Work. 10.3 Suggestions for Effective Implementation. 11 Guidelines 1 Through 8—Model Development. Guideline 1: Apply the Principle of Parsimony. G1.1 Problem. G1.2 Constructive Approaches. Guideline 2: Use a Broad Range of System Information to Constrain the Problem. G2.1 Data Assimilation. G2.2 Using System Information. G2.3 Data Management. G2.4 Application: Characterizing a Fractured Dolomite Aquifer. Guideline 3: Maintain a Well-Posed, Comprehensive Regression Problem. G3.1 Examples. G3.2 Effects of Nonlinearity on the css and pcc. Guideline 4: Include Many Kinds of Data as Observations in the Regression. G4.1 Interpolated “Observations”. G4.2 Clustered Observations. G4.3 Observations that Are Inconsistent with Model Construction. G4.4 Applications: Using Different Types of Observations to Calibrate Groundwater Flow and Transport Models. Guideline 5: Use Prior Information Carefully. G5.1 Use of Prior Information Compared with Observations. G5.2 Highly Parameterized Models. G5.3 Applications: Geophysical Data. Guideline 6: Assign Weights that Reflect Errors. G6.1 Determine Weights. G6.2 Issues of Weighting in Nonlinear Regression. Guideline 7: Encourage Convergence by Making the Model More Accurate and Evaluating the Observations. Guideline 8: Consider Alternative Models. G8.1 Develop Alternative Models. G8.2 Discriminate Between Models. G8.3 Simulate Predictions with Alternative Models. G8.4 Application. 12 Guidelines 9 and 10—Model Testing. Guideline 9: Evaluate Model Fit. G9.1 Determine Model Fit. G9.2 Examine Fit for Existing Observations Important to the Purpose of the Model. G9.3 Diagnose the Cause of Poor Model Fit. Guideline 10: Evaluate Optimized Parameter Values. G10.1 Quantify Parameter-Value Uncertainty. G10.2 Use Parameter Estimates to Detect Model Error. G10.3 Diagnose the Cause of Unreasonable Optimal Parameter Estimates. G10.4 Identify Observations Important to the Parameter Estimates. G10.5 Reduce or Increase the Number of Parameters. 13 Guidelines 11 and 12—Potential New Data. Guideline 11: Identify New Data to Improve Simulated Processes, Features, and Properties. Guideline 12: Identify New Data to Improve Predictions. G12.1 Potential New Data to Improve Features and Properties Governing System Dynamics. G12.2 Potential New Data to Support Observations. 14 Guidelines 13 and 14—Prediction Uncertainty. Guideline 13: Evaluate Prediction Uncertainty and Accuracy Using Deterministic Methods. G13.1 Use Regression to Determine Whether Predicted Values Are Contradicted by the Calibrated Model. G13.2 Use Omitted Data and Postaudits. Guideline 14: Quantify Prediction Uncertainty Using Statistical Methods. G14.1 Inferential Statistics. G14.2 Monte Carlo Methods. 15 Using and Testing the Methods and Guidelines. 15.1 Execution Time Issues. 15.2 Field Applications and Synthetic Test Cases. 15.2.1 The Death Valley Regional Flow System, California and Nevada, USA. 15.2.2 Grindsted Landfill, Denmark. Appendix A: Objective Function Issues. A.1 Derivation of the Maximum-Likelihood Objective Function. A.2 Relation of the Maximum-Likelihood and Least-Squares Objective Functions. A.3 Assumptions Required for Diagonal Weighting to be Correct. A.4 References. Appendix B: Calculation Details of the Modified Gauss–Newton Method. B.1 Vectors and Matrices for Nonlinear Regression. B.2 Quasi-Newton Updating of the Normal Equations. B.3 Calculating the Damping Parameter. B.4 Solving the Normal Equations. B.5 References. Appendix C: Two Important Properties of Linear Regression and the Effects of Nonlinearity. C.1 Identities Needed for the Proofs. C.1.1 True Linear Model. C.1.2 True Nonlinear Model. C.1.3 Linearized True Nonlinear Model. C.1.4 Approximate Linear Model. C.1.5 Approximate Nonlinear Model. C.1.6 Linearized Approximate Nonlinear Model. C.1.7 The Importance of X and X. C.1.8 Considering Many Observations. C.1.9 Normal Equations. C.1.10 Random Variables. C.1.11 Expected Value. C.1.12 Variance–Covariance Matrix of a Vector. C.2 Proof of Property 1: Parameters Estimated by Linear Regression are Unbiased. C.3 Proof of Property 2: The Weight Matrix Needs to be Defined in a Particular Way for Eq. (7.1) to Apply and for the Parameter Estimates to have the Smallest Variance. C.4 References. Appendix D: Selected Statistical Tables. D.1 References. References. Index.

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Book SynopsisEvolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Comprehensive 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 Includes discussion of advanced topics and future research Can be used as a course text or for self-study Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms Trade Review"Deb's book is complete, eminently readable, and the coverage is scholarly and thorough. It is my pleasure and duty to urge you to buy this book, read it, use it and enjoy it." (David E. Goldberg, University of Illinois at Urbana-Champaign, USA) "...discusses two multi-objective optimization procedures, namely the ideal procedure and the preference-based one." (Zentralblatt MATH, Vol. 970, 2001/20) Excerpt from Preface: "...provides an extensive discussion on the principles of multi-objective optimization and on a number of classical approaches." (Mathematical Reviews, 2002) "...As a survey, this book is exemplary and forms an essential resource for EMO researchers at the present time." (Siam Review, Vol.44, No.3, 2002) "...a readable account of a topic of current interest in operational research." (Mathematika, No.48, 2001) ??an outstandingly well-organized and clearly written account of the subject? (The Mathematical Gazette, July 2003)Table of ContentsForeword xv Preface xvii 1 Prologue 1 2 Multi-Objective Optimization 13 3 Classical Methods 49 4 Evolutionary Algorithms 81 5 Non-Elitist Multi-Objective Evolutionary Algorithms 171 6 Elitist Multi-Objective Evolutionary Algorithms 239 7 Constrained Multi-Objective Evolutionary Algorithms 289 8 Salient Issues of Multi-Objective Evolutionary Algorithms 315 9 Applications of Multi-Objective Evolutionary Algorithms 447 10 Epilogue 481 References 489 Index 509

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Book SynopsisSince the bestselling first edition was published, there has been a lot of new research conducted in the area of nonlinear dynamics and chaos. This revised edition provides new material, including a glossary and bibliography, as well as a generous supplement of new figures and illustrations.Trade Review"... much more extensive than before." (The Mathematical Review, March 2004) "The fully updated second edition provides a self-contained introduction to the theory and applications of nonlinear dynamics and chaos." (International Journal of Environmental Analytical Chemistry, Vol.84, No.14 – 15, 10 – 20 December 2004)Table of ContentsPreface vi Preface to the First Edition xv Acknowledgements from the First Edition xxi 1 Introduction 1 1.1 Historical background 1 1.2 Chaotic dynamics in Duffing's oscillator 3 1.3 Attractors and bifurcations 8 Part I Basic Concepts of Nonlinear Dynamics 2 An overview of nonlinear phenomena 15 2.1 Undamped, unforced linear oscillator 15 2.2 Undamped, unforced nonlinear oscillator 17 2.3 Damped, unforced linear oscillator 18 2.4 Damped, unforced nonlinear oscillator 20 2.5 Forced linear oscillator 21 2.6 Forced nonlinear oscillator: periodic attractors 22 2.7 Forced nonlinear oscillator: chaotic attractor 24 3 Point attractors in autonomous systems 26 3.1 The linear oscillator 26 3.2 Nonlinear pendulum oscillations 34 3.3 Evolving ecological systems 41 3.4 Competing point attractors 45 3.5 Attractors of a spinning satellite 47 4 Limit cycles in autonomous systems 50 4.1 The single attractor 50 4.2 Limit cycle in a neural system 51 4.3 Bifurcations of a chemical oscillator 55 4.4 Multiple limit cycles in aeroelastic galloping 58 4.5 Topology of two-dimensional phase space 61 5 Periodic attractors in driven oscillators 62 5.1 The Poincare map 62 5.2 Linear resonance 64 5.3 Nonlinear resonance 66 5.4 The smoothed variational equation 71 5.5 Variational equation for subharmonics 72 5.6 Basins ofattraction by mapping techniques 73 5.7 Resonance ofa self-exciting system 76 5.8 The ABC ofnonlinear dynamics 79 6 Chaotic attractors in forced oscillators 80 6.1 Relaxation oscillations and heartbeat 80 6.2 The Birkhoff±Shaw chaotic attractor 82 6.3 Systems with nonlinear restoring force 93 7 Stability and bifurcations of equilibria and cycles 106 7.1 Liapunov stability and structural stability 106 7.2 Centre manifold theorem 109 7.3 Local bifurcations of equilibrium paths 111 7.4 Local bifurcations of cycles 123 7.5 Basin changes at local bifurcations 126 7.6 Prediction ofincipient instability 128 Part II Iterated Maps as Dynamical Systems 8 Stability and bifurcation of maps 135 8.1 Introduction 135 8.2 Stability of one-dimensional maps 138 8.3 Bifurcations of one-dimensional maps 139 8.4 Stability of two-dimensional maps 149 8.5 Bifurcations of two-dimensional maps 156 8.6 Basin changes at local bifurcations of limit cycles 158 9 Chaotic behaviour of one- and two-dimensional maps 161 9.1 General outline 161 9.2 Theory for one-dimensional maps 164 9.3 Bifurcations to chaos 167 9.4 Bifurcation diagram of one-dimensional maps 170 9.5 He non map 174 Part III Flows, Outstructures, and Chaos 10 The geometry of recurrence 183 10.1 Finite-dimensional dynamical systems 183 10.2 Types ofrecurrent behaviour 187 10.3 Hyperbolic stability types for equilibria 195 10.4 Hyperbolic stability types for limit cycles 200 10.5 Implications ofhyperbolic structure 205 11 The Lorenz system 207 11.1 A model ofthermal convection 207 11.2 First convective instability 209 11.3 The chaotic attractor ofLorenz 214 11.4 Geometry ofa transition to chaos 222 1 2 RoÈssler's band 229 12.1 The simply folded band in an autonomous system 229 12.2 Return map and bifurcations 233 12.3 Smale's horseshoe map 238 12.4 Transverse homoclinic trajectories 243 12.5 Spatial chaos and localized buckling 246 13 Geometry of bifurcations 249 13.1 Local bifurcations 249 13.2 Global bifurcations in the phase plane 258 13.3 Bifurcations of chaotic attractors 266 Part IV Applications in the Physical Sciences 14 Subharmonic resonances of an offshore structure 285 14.1 Basic equation and non-dimensional form 286 14.2 Analytical solution for each domain 288 14.3 Digital computer program 289 14.4 Resonance response curves 290 14.5 Effect of damping 294 14.6 Computed phase projections 296 14.7 Multiple solutions and domains ofattraction 298 15 Chaotic motions of an impacting system 302 15.1 Resonance response curve 302 15.2 Application to moored vessels 306 15.3 Period-doubling and chaotic solutions 306 16 Escape from a potential well 313 16.1 Introduction 313 16.2 Analytical formulation 314 16.3 Overview ofthe steady-state response 319 16.4 The two-band chaotic attractor 324 16.5 Resonance ofthe steady states 328 16.6 Transients and basins ofattraction 333 16.7 Homoclinic phenomena 340 16.8 Heteroclinic phenomena 346 16.9 Indeterminate bifurcations 352 Appendix 359 Illustrated Glossary 369 Bibliography 402 Online Resources 428 Index 429

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Book SynopsisA new, revised edition of a yet unrivaled work on frequency domain analysis Long recognized for his unique focus on frequency domain methods for the analysis of time series data as well as for his applied, easy-to-understand approach, Peter Bloomfield brings his well-known 1976 work thoroughly up to date.Trade Review"These two new books are both new editions of time series classics that will be welcomed by practitioners." (Technometrics, Vol. 42, No. 4, May 2001) "The first edition stood out as a model...this new edition holds up very successfully while incorporating a quarter century of change" (Short Book Reviews, December 2000) "this is a very accessible book" (The Statistician, Vol.50, No.4, 2001)Table of ContentsFitting Sinusoids. The Search for Periodicity. Harmonic Analysis. The Fast Fourier Transform. Examples of Harmonic Analysis. Complex Demodulation. The Spectrum. Some Stationary Time Series Theory. Analysis of Multiple Series. Further Topics. References. Indexes.

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Book SynopsisA balanced presentation of both theoretical and applied material with numerous problem sets to illustrate important concepts. Demonstrates the use of computers and calculators to facilitate problem solving, as well as numerous applications to illustrate basic theory.Table of ContentsProbability. Some Particular Probability Distributions. Nature of Statistical Methods. Sampling Theory. Probability Distributions for Correlation and Regression. Empirical Methods for Correlation and Regression. Sampling Theory. Probability Distributions for Correlation and Regression. Empirical Methods for Correlation and Regression. General Principles for Statistical Inference. Testing Goodness of Fit. Small Sample Distributions. Statistical Design in Experiments. Nonparametric Methods. Other Methods. Appendix. Answers. Tables. Index.

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Book SynopsisThis invaluable resource presents the state of the art in discrete location theory.Table of ContentsIngredients of Locational Analysis (J. Krarup & P.Pruzan). The p-Median Problem and Generalizations (P. Mirchandani). The Uncapacitated Facility Location Problem (G. Cornuejols, etal.). Multiperiod Capacitated Location Models (S. Jacobsen). Decomposition Methods for Facility Location Problems (T. Magnanti& R. Wong). Covering Problems (A. Kolen & A. Tamir). p-Center Problems (G. Handler). Duality: Covering and Constraining p-Center Problems on Trees (B.Tansel, et al.). Locations with Spatial Interactions: The Quadratic AssignmentProblem (R. Burkard). Locations with Spatial Interactions: Competitive Locations andGames (S. Hakimi). Equilibrium Analysis for Voting and Competitive Location Problems(P. Hansen, et al.). Location of Mobile Units in a Stochastic Environment (O. Berman, etal.). Index.

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Book SynopsisThe only way to recommend new crop varieties, agrochemicals and husbandry systems is after they have been thoroughly tested in a series of replicated field trials. The trials, which are used to test these products or systems, need to be designed in such a way that the results obtained are reasonable and representative.Trade Review"...suitable for a practical course to science students wishing to appreciate statistical methods in agricultural and environmental research." (Short Book Reviews, Vol. 21, No. 2, August 2001) "...useful to undergraduate students..." (Zentralblatt MATH, Vol. 961, 2001/11)Table of ContentsPreface. Basic Principles of Experimentation. Basic Statistical Calculations. Basic Data Summary. The Normal Distribution, the t-Distribution and Confidence Intervals. Introduction to Hypothesis Testing. Comparison of Two Independent Sample Means. Linear Regression and Correlation. Curve Fitting. The Completely Randomised Design. The Randomised Block Design. The Latin Square Design. Factorial Experiments. Comparison of Treatment Means. Checking the Assumptions and Transformation of Data. Missing Values and Incomplete Blocks. Split Plot Designs Comparison of Regression Lines and Analysis of Covariance. Analysis of Counts. Some Non-parametric Methods. Appendix 1: The Normal Distribution Function. Appendix 2: Percentage Points of the Normal Distribution. Appendix 3: Percentage Points of the t-Distribution. Appendix 4a: 5 Per Cent Points of the F-Distribution. Appendix 4b: 2.5 Per Cent Points of the F-Distribution. Appendix 4c: 1 Per Cent Points of the F-Distribution. Appendix 4d: 0.1 Per Cent Points of the F-Distribution. Appendix 5: Percentage Points of the Sample Correlation Coefficient (r) When the Population Correlation Coefficient is 0 and n is the Number of X.Y. Pairs. Appendix 6: 5 Per Cent Points of the Studentised Range, for Use in Tukey and SNK Tests. Appendix 7: Percentage Points of the Chi-Square Distribution. Appendix 8: Probabilities of S or Fewer Successes in the Binomial Distribution with n 'trials' and p = 0.5. Appendix 9: Critical Values of T in the Wilcoxon Signed Rank or Matched Pairs Test. Appendix 10: Critical Values of U in the Mann-Whitney Test. References. Further Reading. Index.

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Book SynopsisThis book is concerned with statistical methods for the analysis of data collected from a survey. A survey could consist of data collected from a questionnaire or from measurements, such as those taken as part of a quality control process.Table of ContentsPreface. List of Contributors. Introduction (R. L. Chambers & C. J. Skinner). PART A: APPROACHES TO INFERENCE. Introduction to Part A (R. L.Chambers). Design-based and Model-based Methods for Estimating Model Parameters(David A. Binder and Georgia R. Roberts). The Bayesian Approach to Sample Survey Inference (Roderick J. Little). Interpreting a Sample as Evidence about a Finite Population (Richard Royall). PART B: CATEGORICAL RESPONSE DATA. Introduction to Part B (C. J.Skinner). Analysis of Categorical Response Data from Complex Surveys: an Appraisal and Update (J. N. K. Rao and D. R. Thomas). Fitting Logistic Regression Models in Case-Control Studies with Complex Sampling (Alastair Scott and Chris Wild). PART C: CONTINUOUS AND GENERAL RESPONSE DATA. Introduction to Part C (R. L.Chambers). Graphical Displays of Complex Survey Data through Kernel Smoothing (D. R. Bellhouse, C. M. Goia, and J. E. Stafford) Nonparametric Regression with Complex Survey Data (R. L. Chambers, A. H. Dorfman and M. Yu. Sverchkov). Fitting Generalized Linear Models under Informative Sampling (Danny Pfeffermann and M. Yu. Sverchkov). PART D: LONGITUDINAL DATA. Introduction to Part D (C. J.Skinner). Random Effects Models for Longitudinal Survey Data (C. J.Skinner and D. J.Holmes). Event History Analysis and Longitudinal Surveys (J. F. Lawless). Applying Heterogeneous Transition Models in Labour Economics: the Role of Youth Training in Labour Market Transitions (Fabrizia Mealli and Stephen Pudney). PART E: INCOMPLETE DATA. Introduction to Part E (R. L.Chambers). Bayesian Methods for Unit and Item Nonresponse (Roderick J. Little). Estimation for Multiple Phase Samples (Wayne A. Fuller). Analysis Combining Survey and Geographically Aggregated Data (D. G. Steel, M. Tranmer and D. Holt). References. T. M. F.Smith: Publications up to 2002. Author Index. Subject Index.

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Book SynopsisThis comprehensive, unified text on the principles and practice of clinical trials presents a detailed account of how to conduct the trials. It describes the design, analysis, and interpretation of clinical trials in a non-technical manner and provides a general perspective on their historical development, current status, and future strategy.Table of ContentsIntroduction: The Rationale of Clinical Trials. The Historical Development of Clinical Trials. Organization and Planning. The Justificaton for Randomized Controlled Trials. Methods of Randomization. Blinding and Placebos. Ethical Issues. Crossover Trials. The Size of a Clinical Trial. Monitoring Trial Progress. Forms and Data Management. Protocol Deviations. Basic Principles of Statistical Analysis. Further Aspects of Data Analysis. Publication and Interpretation of Findings. References. Index.

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Book SynopsisProvides an in-depth treatment of the Traveling Salesman problem--the archetypical problem in combinatorial optimization. Each chapter deals with a different aspect of the problem, and has been written by an acknowledged expert in the field. Focusses on the essential ideas in a self-contained manner.Table of ContentsHistory (A. Hoffman and P. Wolfe). Motivation and Modeling (R. Garfinkel). Computational Complexity (D. Johnson and C. Papadimitriou). Well-Solved Special Cases (P. Gilmore, et al.). Performance Guarantees for Heuristics (D. Johnson and C.Papadimitriou). Probabilistic Analysis of Heuristics (R. Karp and J. Steele). Empirical Analysis of Heuristics (B. Golden and W. Stewart). Polyhedral Theory (M. Grotschel and M. Padberg). Polyhedral Algorithms (M. Padberg and M. Grotschel). Branch and Bound Methods (E. Balas and P. Toth). Hamiltonian Cycles (V. Chvatal). Vehicle Routing (N. Christofides). Bibliography.

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Book SynopsisGraphical models----a subset of log--linear models----reveal the interrelationships between multiple variables and features of the underlying conditional independence.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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