Probability and statistics Books

Book SynopsisPresents the fundamentals in probability and statistics along with relevant applications. This book explains the concept of probabilistic modelling and the process of model selection, verification and analysis. It also demonstrates practical problem solving with examples and exercises.Trade Review“For most practising engineers, this book would make a superb reference text, simply because there are so many worked examples, all extremely relevant to engineers.” (Significance, 1 March 2005) Table of ContentsPreface. 1. Introduction. Part A: Probability and Random Variables. 2. Basic Probability Concepts. 3. Random Variables and Probability Distributions. 4. Expectations And Moments. 5. Functions of Random Variables. 6. Some Important Discrete Distributions. 7. Some Important Continuous Distributions. Part B: Statistical Inference, Parameter Estimation, and Model Verification. 8. Observed Data and Graphical Representation. 9. Parameter Estimation. 10. Model Verification. 11. Linear Models and Linear Regression. Appendix A: Tables. Appendix B: Computer Software. Appendix C: Answers to Selected Problems. Subject Index.

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Book SynopsisPresents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. This book includes more than 100 examples and 200 exercises, along with a solutions manual for instructors. It presents the fundamentals in probability and statistics along with their applications.Trade Review“For most practising engineers, this book would make a superb reference text, simply because there are so many worked examples, all extremely relevant to engineers.” (Significance, 1 March 2005) "...the many engineering related examples and exercise problems are a strong feature..." (Technometrics, May 2005) "...designed for students, and as reference for lecturers, the book provides a comprehensive understanding of probability and statistics..." (New Civil Engineer, 18 March, 2004) "...written in an accessible and clear way...gives important techniques of the basic standard methods." (Zentralblatt Math, Vol.1049 2004) "...a good introduction to the ideas of probability and statistics...I would recommend it to anyone as a reference for basic theory..." (Journal of Applied Statistics, Vol 32 (6) August 2005)Table of ContentsPreface. 1. Introduction. Part A: Probability and Random Variables. 2. Basic Probability Concepts. 3. Random Variables and Probability Distributions. 4. Expectations And Moments. 5. Functions of Random Variables. 6. Some Important Discrete Distributions. 7. Some Important Continuous Distributions. Part B: Statistical Inference, Parameter Estimation, and Model Verification. 8. Observed Data and Graphical Representation. 9. Parameter Estimation. 10. Model Verification. 11. Linear Models and Linear Regression. Appendix A: Tables. Appendix B: Computer Software. Appendix C: Answers to Selected Problems. Subject Index.

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Book SynopsisPraise for the First Edition . . . an excellent textbook . . . well organized and neatly written. Mathematical Reviews . . . amazingly interesting . . . Technometrics Thoroughly updated to showcase the interrelationships between probability, statistics, and stochastic processes, Probability, Statistics, and Stochastic Processes, Second Edition prepares readers to collect, analyze, and characterize data in their chosen fields. Beginning with three chapters that develop probability theory and introduce the axioms of probability, random variables, and joint distributions, the book goes on to present limit theorems and simulation. The authors combine a rigorous, calculus-based development of theory with an intuitive approach that appeals to readers'' sense of reason and logic. Including more than 400 examples that help illustrate concepts and theory, the Second Edition features new material on statiTable of ContentsPreface xi Preface to the First Edition xiii 1 Basic Probability Theory 1 1.1 Introduction 1 1.2 Sample Spaces and Events 3 1.3 The Axioms of Probability 7 1.4 Finite Sample Spaces and Combinatorics 15 1.4.1 Combinatorics 17 1.5 Conditional Probability and Independence 27 1.6 The Law of Total Probability and Bayes’ Formula 41 Problems 63 2 Random Variables 76 2.1 Introduction 76 2.2 Discrete Random Variables 77 2.3 Continuous Random Variables 82 2.4 Expected Value and Variance 95 2.5 Special Discrete Distributions 111 2.6 The Exponential Distribution 123 2.7 The Normal Distribution 127 2.8 Other Distributions 131 2.9 Location Parameters 137 2.10 The Failure Rate Function 139 Problems 144 3 Joint Distributions 156 3.1 Introduction 156 3.2 The Joint Distribution Function 156 3.3 Discrete Random Vectors 158 3.4 Jointly Continuous Random Vectors 160 3.5 Conditional Distributions and Independence 164 3.5.1 Independent Random Variables 168 3.6 Functions of Random Vectors 172 3.7 Conditional Expectation 185 3.8 Covariance and Correlation 196 3.9 The Bivariate Normal Distribution 209 3.10 Multidimensional Random Vectors 216 3.11 Generating Functions 231 3.12 The Poisson Process 240 Problems 247 4 Limit Theorems 263 4.1 Introduction 263 4.2 The Law of Large Numbers 264 4.3 The Central Limit Theorem 268 4.4 Convergence in Distribution 275 Problems 278 5 Simulation 281 5.1 Introduction 281 5.2 Random Number Generation 282 5.3 Simulation of Discrete Distributions 283 5.4 Simulation of Continuous Distributions 285 5.5 Miscellaneous 290 Problems 292 6 Statistical Inference 294 6.1 Introduction 294 6.2 Point Estimators 294 6.3 Confidence Intervals 304 6.4 Estimation Methods 312 6.5 Hypothesis Testing 327 6.6 Further Topics in Hypothesis Testing 334 6.7 Goodness of Fit 339 6.8 Bayesian Statistics 351 6.9 Nonparametric Methods 363 Problems 378 7 Linear Models 391 7.1 Introduction 391 7.2 Sampling Distributions 392 7.3 Single Sample Inference 395 7.4 Comparing Two Samples 402 7.5 Analysis of Variance 409 7.6 Linear Regression 415 7.7 The General Linear Model 431 Problems 436 8 Stochastic Processes 444 8.1 Introduction 444 8.2 Discrete -Time Markov Chains 445 8.3 Random Walks and Branching Processes 464 8.4 Continuous -Time Markov Chains 475 8.5 Martingales 494 8.6 Renewal Processes 502 8.7 Brownian Motion 509 Problems 517 Appendix A Tables 527 Appendix B Answers to Selected Problems 535 Further Reading 551 Index 553

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Book SynopsisA complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: LinearTrade Review“I found this book highly informative and interesting to read. The proper mix of theory and hands-on programming examples makes it recommended reading for both R programmers interested in finance and financial analysts with a basic programming background. Well written and following a clear and defined logical layout, the author has written a current reference text on using a powerful open-source programming language for typical financial analysis.” (Computing Reviews, 25 March 2014) “All in all, this book is a good and useful introduction to financial time series with many real-world examples. It is suitable for use both as a textbook and for self-study, with exercises provided at the end of each chapter.” (International Statistical Review, 14 June 2013) Table of ContentsPreface xiii 1 FINANCIAL DATA AND THEIR PROPERTIES 1 1.1 Asset Returns 2 1.2 Bond Yields and Prices 7 1.3 Implied Volatility 10 1.4 R Packages and Demonstrations 12 1.4.1 Installation of R Packages 12 1.4.2 The Quantmod Package 12 1.4.3 Some Basic R Commands 16 1.5 Examples of Financial Data 17 1.6 Distributional Properties of Returns 20 1.6.1 Review of Statistical Distributions and Their Moments 20 1.7 Visualization of Financial Data 27 1.8 Some Statistical Distributions 32 1.8.1 Normal Distribution 32 1.8.2 Lognormal Distribution 32 1.8.3 Stable Distribution 33 1.8.4 Scale Mixture of Normal Distributions 33 1.8.5 Multivariate Returns 34 Exercises 36 References 37 2 LINEAR MODELS FOR FINANCIAL TIME SERIES 39 2.1 Stationarity 40 2.2 Correlation and Autocorrelation Function 43 2.3 White Noise and Linear Time Series 50 2.4 Simple Autoregressive Models 51 2.4.1 Properties of AR Models 52 2.4.2 Identifying AR Models in Practice 60 2.4.3 Goodness of Fit 67 2.4.4 Forecasting 67 2.5 Simple Moving Average Models 69 2.5.1 Properties of MA Models 72 2.5.2 Identifying MA Order 73 2.5.3 Estimation 74 2.5.4 Forecasting Using MA Models 75 2.6 Simple ARMA Models 78 2.6.1 Properties of ARMA(1,1) Models 79 2.6.2 General ARMA Models 80 2.6.3 Identifying ARMA Models 81 2.6.4 Forecasting Using an ARMA Model 84 2.6.5 Three Model Representations for an ARMA Model 84 2.7 Unit-Root Nonstationarity 86 2.7.1 Random Walk 86 2.7.2 Random Walk with Drift 88 2.7.3 Trend-Stationary Time Series 90 2.7.4 General Unit-Root Nonstationary Models 91 2.7.5 Unit-Root Test 91 2.8 Exponential Smoothing 96 2.9 Seasonal Models 98 2.9.1 Seasonal Differencing 99 2.9.2 Multiplicative Seasonal Models 101 2.9.3 Seasonal Dummy Variable 107 2.10 Regression Models with Time Series Errors 110 2.11 Long-Memory Models 117 2.12 Model Comparison and Averaging 120 2.12.1 In-sample Comparison 120 2.12.2 Out-of-sample Comparison 121 2.12.3 Model Averaging 125 Exercises 125 References 127 3 CASE STUDIES OF LINEAR TIME SERIES 128 3.1 Weekly Regular Gasoline Price 129 3.1.1 Pure Time Series Model 130 3.1.2 Use of Crude Oil Prices 133 3.1.3 Use of Lagged Crude Oil Prices 134 3.1.4 Out-of-Sample Predictions 135 3.2 Global Temperature Anomalies 140 3.2.1 Unit-Root Stationarity 141 3.2.2 Trend-Nonstationarity 145 3.2.3 Model Comparison 148 3.2.4 Long-Term Prediction 150 3.2.5 Discussion 153 3.3 US Monthly Unemployment Rates 157 3.3.1 Univariate Time Series Models 157 3.3.2 An Alternative Model 161 3.3.3 Model Comparison 165 3.3.4 Use of Initial Jobless Claims 165 3.3.5 Comparison 173 Exercises 174 References 175 4 ASSET VOLATILITY AND VOLATILITY MODELS 176 4.1 Characteristics of Volatility 177 4.2 Structure of a Model 178 4.3 Model Building 181 4.4 Testing for ARCH Effect 182 4.5 The ARCH Model 185 4.5.1 Properties of ARCH Models 186 4.5.2 Advantages and Weaknesses of ARCH Models 187 4.5.3 Building an ARCH Model 188 4.5.4 Some Examples 193 4.6 The GARCH Model 199 4.6.1 An Illustrative Example 201 4.6.2 Forecasting Evaluation 210 4.6.3 A Two-Pass Estimation Method 210 4.7 The Integrated GARCH Model 211 4.8 The GARCH-M Model 213 4.9 The Exponential Garch Model 215 4.9.1 An Illustrative Example 217 4.9.2 An Alternative Model Form 218 4.9.3 Second Example 218 4.9.4 Forecasting Using an EGARCH Model 220 4.10 The Threshold Garch Model 222 4.11 Asymmetric Power ARCH Models 224 4.12 Nonsymmetric GARCH Model 226 4.13 The Stochastic Volatility Model 228 4.14 Long-Memory Stochastic Volatility Models 230 4.15 Alternative Approaches 232 4.15.1 Use of High Frequency Data 232 4.15.2 Use of Daily Open, High, Low, and Close Prices 235 Exercises 239 References 241 5 APPLICATIONS OF VOLATILITY MODELS 243 5.1 Garch Volatility Term Structure 244 5.1.1 Term Structure 246 5.2 Option Pricing and Hedging 248 5.3 Time-Varying Correlations and Betas 251 5.3.1 Time-Varying Betas 256 5.4 Minimum Variance Portfolios 259 5.5 Prediction 263 Exercises 271 References 272 6 HIGH FREQUENCY FINANCIAL DATA 274 6.1 Nonsynchronous Trading 275 6.2 Bid–Ask Spread of Trading Prices 279 6.3 Empirical Characteristics of Trading Data 282 6.4 Models for Price Changes 285 6.4.1 Ordered Probit Model 288 6.4.2 A Decomposition Model 293 6.5 Duration Models 298 6.5.1 Diurnal Component 299 6.5.2 The ACD Model 301 6.5.3 Estimation 303 6.6 Realized Volatility 308 6.6.1 Handling Microstructure Noises 313 6.6.2 Discussion 317 Appendix A: Some Probability Distributions 320 Appendix B: Hazard Function 323 Exercises 324 References 325 7 VALUE AT RISK 327 7.1 Risk Measure and Coherence 328 7.1.1 Value at Risk (VaR) 329 7.1.2 Expected Shortfall 334 7.2 Remarks on Calculating Risk Measures 336 7.3 Riskmetrics 337 7.3.1 Discussion 342 7.3.2 Multiple Positions 343 7.4 An Econometric Approach 345 7.4.1 Multiple Periods 348 7.5 Quantile Estimation 352 7.5.1 Quantile and Order Statistics 353 7.5.2 Quantile Regression 354 7.6 Extreme Value Theory 358 7.6.1 Review of Extreme Value Theory 358 7.6.2 Empirical Estimation 361 7.6.3 Application to Stock Returns 363 7.7 An Extreme Value Approach to Var 368 7.7.1 Discussion 370 7.7.2 Multiperiod VaR 371 7.7.3 Return Level 371 7.8 Peaks Over Thresholds 372 7.8.1 Statistical Theory 373 7.8.2 Mean Excess Function 374 7.8.3 Estimation 376 7.8.4 An Alternative Parameterization 378 7.9 The Stationary Loss Processes 381 Exercises 383 References 384 Index 387

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Book Synopsis* Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process.

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Book SynopsisAn insightful, hands-on focus on the statistical methods used by compensation and human resources professionals in their everyday work Across various industries, compensation professionals work to organize and analyze aspects of employment that deal with elements of pay, such as deciding base salary, bonus, and commission provided by an employer to its employees for work performed. Acknowledging the numerous quantitative analyses of data that are a part of this everyday work, Statistics for Compensation provides a comprehensive guide to the key statistical tools and techniques needed to perform those analyses and to help organizations make fully informed compensation decisions. This self-contained book is the first of its kind to explore the use of various quantitative methodsfrom basic notions about percents to multiple linear regressionthat are used in the management, design, and implementation of powerful compensation strategies. Drawing upon his exteTrade Review“As an experienced compensation manager for a publicly traded Fortune 500 company, I have found this book to be an all-inclusive, highly useful and infor­mative desk reference. It certainly has been extremely valuable in helping me to contribute to successful strategic decisions at my company.” (Workspan, 1 January 2013) "The book can serve as a text for students specializing in compensation or human resources, or as a reference for practitioners. He provides worked examples throughout." (Booknews, 1 June 2011) Table of ContentsPreface xiii Chapter 1 Introduction 1 1.1 Why do Statistical Analysis? 2 Example Analysis 3 1.2 Statistics 5 1.3 Numbers Raise Issues 6 1.4 Behind Every Data Point, There Is a Story 8 1.5 Aggressive Inquisitiveness 9 1.6 Model Building Framework 9 Example Model 10 1.7 Data Sets 10 1.8 Prerequisites 11 Chapter 2 Basic Notions 13 2.1 Percent 14 Graphical Displays of Percents 16 2.2 Percent Difference 21 2.3 Compound Interest 23 Future Value 24 Present Value 26 Translating 27 Practice Problems 28 Chapter 3 Frequency Distributions and Histograms 31 3.1 Definitions and Construction 41 Rules for Categories 43 3.2 Comparing Distributions 48 Absolute Comparison and Relative Comparison 48 Comparing More Than Two Distributions 50 3.3 Information Loss and Comprehension Gain 51 3.4 Category Selection 51 3.5 Distribution Shapes 54 Uniform Distribution 55 Bell-Shaped Distribution 55 Normal Distribution 56 Skewed Distribution 59 Bimodal Distribution 60 Practice Problems 62 Chapter 4 Measures of Location 67 4.1 Mode 67 4.2 Median 68 4.3 Mean 70 4.4 Trimmed Mean 73 4.5 Overall Example and Comparison 73 Comparison 75 4.6 Weighted and Unweighted Average 76 Which Measure to Use? 78 Application of Weighted Averages to Salary Increase Guidelines 80 4.7 Simpson’s Paradox 82 4.8 Percentile 85 Reverse Percentile 88 4.9 Percentile Bars 90 Practice Problems 92 Chapter 5 Measures of Variability 95 5.1 Importance of Knowing Variability 95 5.2 Population and Sample 96 Examples of Populations 96 Examples of Samples and Populations 96 5.3 Types of Samples 97 5.4 Standard Deviation 98 Interpretations and Applications of Standard Deviation 100 5.5 Coefficient of Variation 107 Interpretations and Applications of Coefficient of Variation 108 5.6 Range 109 Interpretations and Applications of Range 109 5.7 P90/P10 110 Interpretations and Applications of P90/P10 111 5.8 Comparison and Summary 112 Practice Problems 115 Chapter 6 Model Building 119 6.1 Prelude to Models 119 6.2 Introduction 120 6.3 Scientific Method 122 6.4 Models 123 6.5 Model Building Process 126 Plotting Points 128 Functional Forms 132 Method of Least Squares 136 Practice Problems 138 Chapter 7 Linear Model 141 7.1 Examples 141 7.2 Straight Line Basics 143 Interpretations of Intercept and Slope 144 Using the Equation 145 7.3 Fitting the Line to the Data 147 What We Are Predicting 148 Interpretations of Intercept and Slope 149 7.4 Model Evaluation 149 Appearance 150 Coefficient of Determination 150 Correlation 152 Standard Error of Estimate 154 Common Sense 154 7.5 Summary of Interpretations and Evaluation 155 7.6 Cautions 155 7.7 Digging Deeper 158 7.8 Keep the Horse before the Cart 160 Practice Problems 164 Chapter 8 Exponential Model 167 8.1 Examples 167 8.2 Logarithms 168 Antilogs 170 Scales 170 Why Logarithms? 171 8.3 Exponential Model 172 8.4 Model Evaluation 176 Appearance 176 Coefficient of Determination 177 Correlation 177 Standard Error of Estimate 177 Common Sense 178 Summary of Evaluation 178 Practice Problems 178 Chapter 9 Maturity Curve Model 181 9.1 Maturity Curves 181 9.2 Building the Model 184 Cubic Model 184 Cubic Model Evaluation 186 Spline Model 187 Spline Model Evaluation 188 9.3 Comparison of Models 190 Practice Problems 190 Chapter 10 Power Model 193 10.1 Building the Model 193 10.2 Model Evaluation 197 Appearance 197 Coefficient of Determination 198 Correlation 198 Standard Error of Estimate 198 Common Sense 199 Summary of Evaluation 199 Practice Problems 200 Chapter 11 Market Models and Salary Survey Analysis 201 11.1 Introduction 201 11.2 Commonalities of Approaches 203 11.3 Final Market-Based Salary Increase Budget 205 Initial Market-Based Salary Increase Budget and Market Position 205 Final Market-Based Salary Increase Budget 206 Raises Given Throughout the Year 206 Raises Given on a Common Date 208 11.4 Other Factors Influencing the Final Salary Increase Budget Recommendation 210 Assumptions 211 11.5 Salary Structure 211 Practice Problems 213 Chapter 12 Integrated Market Model: Linear 215 12.1 Gather Market Data 215 12.2 Age Data to a Common Date 217 12.3 Create an Integrated Market Model 217 Interpretations 219 12.4 Compare Employee Pay with Market Model 222 Practice Problems 228 Chapter 13 Integrated Market Model: Exponential 233 Practice Problems 246 Chapter 14 Integrated Market Model: Maturity Curve 251 Practice Problems 261 Chapter 15 Job Pricing Market Model: Group of Jobs 265 Practice Problems 272 Chapter 16 Job Pricing Market Model: Power Model 277 Practice Problems 280 Chapter 17 Multiple Linear Regression 283 17.1 What It Is 283 17.2 Similarities and Differenceswith Simple Linear Regression 284 17.3 Building the Model 285 First x-Variable 292 Second x-Variable 295 Standardized Coefficient 298 Third x-Variable 300 Multicollinearity 301 17.4 Model Evaluation 305 Regression Coefficients 305 Standardized Coefficients 306 Coefficient of Determination 306 Standard Error of Estimate 306 Multicollinearity 306 Simplicity 307 Common Sense 307 Acceptability 307 Reality 307 Decision 307 17.5 Mixed Messages in Evaluating A Model 308 r2 Versus Common Sense 308 r2 Versus Simplicity 308 Simplicity Versus Acceptability 308 17.6 Summary of Regressions 308 17.7 Digging Deeper 310 Summary 315 Practice Problems 317 Appendix 319 A.1 Value Exchange Theory 319 Achieving Organization Goals 319 Value Exchange 319 A Fair Value Exchange Is a Good Deal 320 A.2 Factors Determining a Person’s Pay 321 System Factors 322 Individual Factors 323 A.3 Types of Numbers 324 Definitions and Properties 324 Histograms with All Four Types of Measurements 327 A.4 Significant Figures 330 A.5 Scientific Notation 331 A.6 Accuracy and Precision 332 Which Is More Important? 333 A.7 Compound Interest–Additional 333 Other Formulas 333 A.8 Rule of 72 334 Derivation of the Rule of 72 335 A.9 Normal Distribution 336 Central Limit Theorem 337 Distribution of Salary Survey Data 338 A.10 Linear Regression Technical Note 338 A.11 Formulas for Regression Terms 340 A.12 Logarithmic Conversion 340 A.13 Range Spread Relationships 340 Overlap 343 A.14 Statistical Inference in Regression 344 t-Statistic and Its Probability 347 F-Statistic and Its Probability 348 Mixed Messages in Evaluating a Model 349 A.15 Additional Multiple Linear Regression Topics 349 Adjusted r2 349 Coding of Indicator Variables 350 Interaction Terms 351 GLOSSARY 357 REFERENCES 369 ANSWERS TO PRACTICE PROBLEMS 371 INDEX 433

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Book SynopsisStatistics in Psychology covers all statistical methods needed in education and research in psychology. This book looks at research questions when planning data sampling, that is to design the intended study and to calculate the sample sizes in advance.Table of ContentsIntroduction. 1 Concept of the Book. 2 Measuring in Psychology. 2.1 Types of psychological measurements. 2.2 Measurement techniques in psychological assessment. 2.3 Quality criteria in psychometrics. 2.4 Additional psychological measurement techniques. 2.5 Statistical models of measurement with psychological roots. 3 Psychology: An Empirical Science. 3.1 Gain of insight in psychology. 3.2 Steps of empirical research. 4 Definition: Character, Chance, Experiment, and Survey. 4.1 Nominal scale. 4.2 Ordinal scale. 4.3 Interval scale. 4.4 Ratio scale. 4.5 Characters and factors. II Descriptive Statistics. 5 Numerical and graphical Data Analysis. 5.1 Introduction to data analysis. 5.2 Frequencies and empirical distributions. 5.3 Statistics. 5.4 Frequency distribution for several characters. III Inferential Statistics for one Character. 6 Probability and distribution. 6.1 Relative frequencies and probabilities. 6.2 Random variable and theoretical distributions. 6.3 Quantiles of theoretical distribution functions. 6.4 Mean and variance of theoretical distributions. 6.5 Estimation of unknown parameters. 7 Assumptions: Random Sampling and Randomization. 7.1 Simple random sampling in surveys. 7.2 Principles of random sampling and randomization. 8 One Sample from one Population. 8.1 Introduction. 8.2 The Parameter mof acharacter modeled by a normally distributed random variable. 8.3 Planning a study for hypothesis testing with respect to m. 8.4 Sequential tests for the unknown parameter m. 8.5 Estimation, hypothesis testing, planning the study, and sequential testing concerning other parameters. 9 Two Samples from two Populations. 9.1 Hypothesis testing, study planning and sequential testing regarding the unknown parameters m1 and m2. 9.2 Hypothesis testing, study planning and sequential testing for other parameters. 9.3 Equivalence testing. 10 Samples from more than two Populations. 10.1 The various problem situations. 10.2. Selection procedures. 10.3 Multiple comparisons of means. 10.4 Analysis of variance. IV Descriptive and Inferential Statistics for two Characters. 11 Regression and Correlation. 11.1 Introduction. 11.2 Regression model. 11.3 Correlation coefficients and measures of association. 11.4 Hypothesis testing and planning the study concerning correlation coefficients. 11.5 Correlation analysis in two samples. V Inferential Statistics for more than two Characters. 12 One Sample from one Population. 12.1 Association between three or more characters. 12.2 Hypothesis testing concerning a vector of means m. 12.3 Comparisons of means and "homological" methods for matched observations. 13 Samples from more than one Population. 13.1 General linear model. 13.2 Analysis of covariance. 13.3. Multivariate analysis of variance. 13.4 Discriminant analysis. VI Model Generation and Theory-Generating Procedures. 14 Model Generation. 14.1 Theoretical basics of model generation. 14.2 Methods for determining the quality and excellence of a model. 14.2.1 Goodness of fit tests. 14.2.2 Coefficients of the goodness of fit. 14.2.3 Cross-validation. 14.4 Simulation: Non-analytical solutions to statistical problems. 15 Theory-Generating Procedures. 15.1 Descriptive statistics' methods. 15.2 Methods of inferential statistics.

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Book SynopsisModern Analysis of Customer Surveys: with applications using R Customer survey studies deal with customer, consumer and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. This book demonstrates how integrating such basic analysis with more advanced tools, provides insights into non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey. Key features: Provides an integrated case studies-based approach to analysing customer survey data. Presents a general introduction to customer surveys, within an organization's business cycle. Contains classical techniques with modern and non standard tools.Table of ContentsForeword xvii Preface xix Contributors xxiii Part I Basic Aspects of Customer Satisfaction Survey Data Analysis 1 Standards and Classical Techniques in Data Analysis of Customer Satisfaction Surveys 3 Silvia Salini and Ron S. Kenett 1.1 Literature on customer satisfaction surveys 4 1.2 Customer satisfaction surveys and the business cycle 4 1.3 Standards used in the analysis of survey data 7 1.4 Measures and models of customer satisfaction 12 1.4.1 The conceptual construct 12 1.4.2 The measurement process 13 1.5 Organization of the book 15 1.6 Summary 17 References 17 2 The ABC Annual Customer Satisfaction Survey 19 Ron S. Kenett and Silvia Salini 2.1 The ABC company 19 2.2 ABC 2010 ACSS: Demographics of respondents 20 2.3 ABC 2010 ACSS: Overall satisfaction 22 2.4 ABC 2010 ACSS: Analysis of topics 24 2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27 2.6 Summary 28 References 28 Appendix 29 3 Census and Sample Surveys 37 Giovanna Nicolini and Luciana Dalla Valle 3.1 Introduction 37 3.2 Types of surveys 39 3.2.1 Census and sample surveys 39 3.2.2 Sampling design 40 3.2.3 Managing a survey 40 3.2.4 Frequency of surveys 41 3.3 Non-sampling errors 41 3.3.1 Measurement error 42 3.3.2 Coverage error 42 3.3.3 Unit non-response and non-self-selection errors 43 3.3.4 Item non-response and non-self-selection error 44 3.4 Data collection methods 44 3.5 Methods to correct non-sampling errors 46 3.5.1 Methods to correct unit non-response errors 46 3.5.2 Methods to correct item non-response 49 3.6 Summary 51 References 52 4 Measurement Scales 55 Andrea Bonanomi and Gabriele Cantaluppi 4.1 Scale construction 55 4.1.1 Nominal scale 56 4.1.2 Ordinal scale 57 4.1.3 Interval scale 58 4.1.4 Ratio scale 59 4.2 Scale transformations 60 4.2.1 Scale transformations referred to single items 61 4.2.2 Scale transformations to obtain scores on a unique interval scale 66 Acknowledgements 69 References 69 5 Integrated Analysis 71 Silvia Biffignandi 5.1 Introduction 71 5.2 Information sources and related problems 73 5.2.1 Types of data sources 73 5.2.2 Advantages of using secondary source data 73 5.2.3 Problems with secondary source data 74 5.2.4 Internal sources of secondary information 75 5.3 Root cause analysis 78 5.3.1 General concepts 78 5.3.2 Methods and tools in RCA 81 5.3.3 Root cause analysis and customer satisfaction 85 5.4 Summary 87 Acknowledgement 87 References 87 6 Web Surveys 89 Roberto Furlan and Diego Martone 6.1 Introduction 89 6.2 Main types of web surveys 90 6.3 Economic benefits of web survey research 91 6.3.1 Fixed and variable costs 92 6.4 Non-economic benefits of web survey research 94 6.5 Main drawbacks of web survey research 96 6.6 Web surveys for customer and employee satisfaction projects 100 6.7 Summary 102 References 102 7 The Concept and Assessment of Customer Satisfaction 107 Irena Ograjenšek and Iddo Gal 7.1 Introduction 107 7.2 The quality–satisfaction–loyalty chain 108 7.2.1 Rationale 108 7.2.2 Definitions of customer satisfaction 108 7.2.3 From general conceptions to a measurement model of customer satisfaction 110 7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context 112 7.2.5 From customer satisfaction to customer loyalty 113 7.3 Customer satisfaction assessment: Some methodological considerations 115 7.3.1 Rationale 115 7.3.2 Think big: An assessment programme 115 7.3.3 Back to basics: Questionnaire design 116 7.3.4 Impact of questionnaire design on interpretation 118 7.3.5 Additional concerns in the B2B setting 119 7.4 The ABC ACSS questionnaire: An evaluation 119 7.4.1 Rationale 119 7.4.2 Conceptual issues 119 7.4.3 Methodological issues 120 7.4.4 Overall ABC ACSS questionnaire asssessment 121 7.5 Summary 121 References 122 Appendix 126 8 Missing Data and Imputation Methods 129 Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin 8.1 Introduction 129 8.2 Missing-data patterns and missing-data mechanisms 131 8.2.1 Missing-data patterns 131 8.2.2 Missing-data mechanisms and ignorability 132 8.3 Simple approaches to the missing-data problem 134 8.3.1 Complete-case analysis 134 8.3.2 Available-case analysis 135 8.3.3 Weighting adjustment for unit nonresponse 135 8.4 Single imputation 136 8.5 Multiple imputation 138 8.5.1 Multiple-imputation inference for a scalar estimand 138 8.5.2 Proper multiple imputation 139 8.5.3 Appropriately drawing imputations with monotone missing-data patterns 140 8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns 141 8.5.5 Multiple imputation in practice 142 8.5.6 Software for multiple imputation 143 8.6 Model-based approaches to the analysis of missing data 144 8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145 8.8 Summary 149 Acknowledgements 150 References 150 9 Outliers and Robustness for Ordinal Data 155 Marco Riani, Francesca Torti and Sergio Zani 9.1 An overview of outlier detection methods 155 9.2 An example of masking 157 9.3 Detection of outliers in ordinal variables 159 9.4 Detection of bivariate ordinal outliers 160 9.5 Detection of multivariate outliers in ordinal regression 161 9.5.1 Theory 161 9.5.2 Results from the application 163 9.6 Summary 168 References 168 Part II Modern Techniques in Customer Satisfaction Survey Data Analysis 10 Statistical Inference for Causal Effects 173 Fabrizia Mealli, Barbara Pacini and Donald B. Rubin 10.1 Introduction to the potential outcome approach to causal inference 173 10.1.1 Causal inference primitives: Units, treatments, and potential outcomes 175 10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption 176 10.1.3 Defining causal estimands 177 10.2 Assignment mechanisms 179 10.2.1 The criticality of the assignment mechanism 179 10.2.2 Unconfounded and strongly ignorable assignment mechanisms 180 10.2.3 Confounded and ignorable assignment mechanisms 181 10.2.4 Randomized and observational studies 181 10.3 Inference in classical randomized experiments 182 10.3.1 Fisher’s approach and extensions 183 10.3.2 Neyman’s approach to randomization-based inference 183 10.3.3 Covariates, regression models, and Bayesian model-based inference 184 10.4 Inference in observational studies 185 10.4.1 Inference in regular designs 186 10.4.2 Designing observational studies: The role of the propensity score 186 10.4.3 Estimation methods 188 10.4.4 Inference in irregular designs 188 10.4.5 Sensitivity and bounds 189 10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs 189 References 190 11 Bayesian Networks Applied to Customer Surveys 193 Ron S. Kenett, Giovanni Perruca and Silvia Salini 11.1 Introduction to Bayesian networks 193 11.2 The Bayesian network model in practice 197 11.2.1 Bayesian network analysis of the ABC 2010 ACSS 197 11.2.2 Transport data analysis 201 11.2.3 R packages and other software programs used for studying BNs 210 11.3 Prediction and explanation 211 11.4 Summary 213 References 213 12 Log-linear Model Methods 217 Stephen E. Fienberg and Daniel Manrique-Vallier 12.1 Introduction 217 12.2 Overview of log-linear models and methods 218 12.2.1 Two-way tables 218 12.2.2 Hierarchical log-linear models 220 12.2.3 Model search and selection 222 12.2.4 Sparseness in contingency tables and its implications 223 12.2.5 Computer programs for log-linear model analysis 223 12.3 Application to ABC survey data 224 12.4 Summary 227 References 228 13 CUB Models: Statistical Methods and Empirical Evidence 231 Maria Iannario and Domenico Piccolo 13.1 Introduction 231 13.2 Logical foundations and psychological motivations 233 13.3 A class of models for ordinal data 233 13.4 Main inferential issues 236 13.5 Specification of CUB models with subjects’ covariates 238 13.6 Interpreting the role of covariates 240 13.7 A more general sampling framework 241 13.7.1 Objects’ covariates 241 13.7.2 Contextual covariates 243 13.8 Applications of CUB models 244 13.8.1 Models for the ABC annual customer satisfaction survey 245 13.8.2 Students’ satisfaction with a university orientation service 246 13.9 Further generalizations 248 13.10 Concluding remarks 251 Acknowledgements 251 References 251 Appendix 255 A program in R for CUB models 255 A.1 Main structure of the program 255 A.2 Inference on CUB models 255 A.3 Output of CUB models estimation program 256 A.4 Visualization of several CUB models in the parameter space 257 A.5 Inference on CUB models in a multi-object framework 257 A.6 Advanced software support for CUB models 258 14 The Rasch Model 259 Francesca De Battisti, Giovanna Nicolini and Silvia Salini 14.1 An overview of the Rasch model 259 14.1.1 The origins and the properties of the model 259 14.1.2 Rasch model for hierarchical and longitudinal data 263 14.1.3 Rasch model applications in customer satisfaction surveys 265 14.2 The Rasch model in practice 267 14.2.1 Single model 267 14.2.2 Overall model 268 14.2.3 Dimension model 272 14.3 Rasch model software 277 14.4 Summary 278 References 279 15 Tree-based Methods and Decision Trees 283 Giuliano Galimberti and Gabriele Soffritti 15.1 An overview of tree-based methods and decision trees 283 15.1.1 The origins of tree-based methods 283 15.1.2 Tree graphs, tree-based methods and decision trees 284 15.1.3 CART 287 15.1.4 CHAID 293 15.1.5 PARTY 295 15.1.6 A comparison of CART, CHAID and PARTY 297 15.1.7 Missing values 297 15.1.8 Tree-based methods for applications in customer satisfaction surveys 298 15.2 Tree-based methods and decision trees in practice 300 15.2.1 ABC ACSS data analysis with tree-based methods 300 15.2.2 Packages and software implementing tree-based methods 303 15.3 Further developments 304 References 304 16 PLS Models 309 Giuseppe Boari and Gabriele Cantaluppi 16.1 Introduction 309 16.2 The general formulation of a structural equation model 310 16.2.1 The inner model 310 16.2.2 The outer model 312 16.3 The PLS algorithm 313 16.4 Statistical interpretation of PLS 319 16.5 Geometrical interpretation of PLS 320 16.6 Comparison of the properties of PLS and LISREL procedures 321 16.7 Available software for PLS estimation 323 16.8 Application to real data: Customer satisfaction analysis 323 References 329 17 Nonlinear Principal Component Analysis 333 Pier Alda Ferrari and Alessandro Barbiero 17.1 Introduction 333 17.2 Homogeneity analysis and nonlinear principal component analysis 334 17.2.1 Homogeneity analysis 334 17.2.2 Nonlinear principal component analysis 336 17.3 Analysis of customer satisfaction 338 17.3.1 The setting up of indicator 338 17.3.2 Additional analysis 340 17.4 Dealing with missing data 340 17.5 Nonlinear principal component analysis versus two competitors 343 17.6 Application to the ABC ACSS data 344 17.6.1 Data preparation 344 17.6.2 The homals package 345 17.6.3 Analysis on the ‘complete subset’ 346 17.6.4 Comparison of NLPCA with PCA and Rasch analysis 350 17.6.5 Analysis of ‘entire data set’ for the comparison of missing data treatments 352 17.7 Summary 355 References 355 18 Multidimensional Scaling 357 Nadia Solaro 18.1 An overview of multidimensional scaling techniques 357 18.1.1 The origins of MDS models 358 18.1.2 MDS input data 359 18.1.3 MDS models 362 18.1.4 Assessing the goodness of MDS solutions 369 18.1.5 Comparing two MDS solutions: Procrustes analysis 371 18.1.6 Robustness issues in the MDS framework 371 18.1.7 Handling missing values in MDS framework 373 18.1.8 MDS applications in customer satisfaction surveys 373 18.2 Multidimensional scaling in practice 374 18.2.1 Data sets analysed 375 18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set 375 18.2.3 Weighting objects or items 381 18.2.4 Robustness analysis with the forward search 382 18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set 383 18.2.6 Package and software for MDS methods 384 18.3 Multidimensional scaling in a future perspective 386 18.4 Summary 386 References 387 19 Multilevel Models for Ordinal Data 391 Leonardo Grilli and Carla Rampichini 19.1 Ordinal variables 391 19.2 Standard models for ordinal data 393 19.2.1 Cumulative models 394 19.2.2 Other models 395 19.3 Multilevel models for ordinal data 395 19.3.1 Representation as an underlying linear model with thresholds 396 19.3.2 Marginal versus conditional effects 397 19.3.3 Summarizing the cluster-level unobserved heterogeneity 397 19.3.4 Consequences of adding a covariate 398 19.3.5 Predicted probabilities 399 19.3.6 Cluster-level covariates and contextual effects 399 19.3.7 Estimation of model parameters 400 19.3.8 Inference on model parameters 401 19.3.9 Prediction of random effects 402 19.3.10 Software 403 19.4 Multilevel models for ordinal data in practice: An application to student ratings 404 References 408 20 Quality Standards and Control Charts Applied to Customer Surveys 413 Ron S. Kenett, Laura Deldossi and Diego Zappa 20.1 Quality standards and customer satisfaction 413 20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414 20.3 Control Charts and ISO 7870 417 20.4 Control charts and customer surveys: Standard assumptions 420 20.4.1 Introduction 420 20.4.2 Standard control charts 420 20.5 Control charts and customer surveys: Non-standard methods 426 20.5.1 Weights on counts: Another application of the c chart 426 20.5.2 The χ2 chart 427 20.5.3 Sequential probability ratio tests 428 20.5.4 Control chart over items: A non-standard application of SPC methods 429 20.5.5 Bayesian control chart for attributes: A modern application of SPC methods 432 20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated 433 20.6 The M-test for assessing sample representation 433 20.7 Summary 435 References 436 21 Fuzzy Methods and Satisfaction Indices 439 Sergio Zani, Maria Adele Milioli and Isabella Morlini 21.1 Introduction 439 21.2 Basic definitions and operations 440 21.3 Fuzzy numbers 441 21.4 A criterion for fuzzy transformation of variables 443 21.5 Aggregation and weighting of variables 445 21.6 Application to the ABC customer satisfaction survey data 446 21.6.1 The input matrices 446 21.6.2 Main results 448 21.7 Summary 453 References 455 Appendix an Introduction to R 457 Stefano Maria Iacus A.1 Introduction 457 A.2 How to obtain R 457 A.3 Type rather than ‘point and click’ 458 A.3.1 The workspace 458 A.3.2 Graphics 458 A.3.3 Getting help 459 A.3.4 Installing packages 459 A.4 Objects 460 A.4.1 Assignments 460 A.4.2 Basic object types 462 A.4.3 Accessing objects and subsetting 466 A.4.4 Coercion between data types 469 A.5 S4 objects 470 A.6 Functions 472 A.7 Vectorization 473 A.8 Importing data from different sources 475 A.9 Interacting with databases 476 A.10 Simple graphics manipulation 477 A.11 Basic analysis of the ABC data 481 A.12 About this document 496 A.13 Bibliographical notes 496 References 496 Index 499

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Book SynopsisLatent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples.Trade Review“Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective.” (Mathematical Reviews, 2012) "Statistical techniques to study the nature and interpretation of a latent variable should be highly useful for researchers and practitioners across several fields. The third edition of this book is comprehensive and provides a solid foundation for understanding these techniques, and is strongly recommended." (Book Pleasures, 2012)Table of ContentsPreface xi Acknowledgements xv 1 Basic Ideas and Examples 1 1.1 The statistical problem 1 1.2 The basic idea 3 1.3 Two Examples 4 1.4 A broader theoretical view 6 1.5 Illustration of an alternative approach 8 1.6 An overview of special cases 10 1.7 Principal components 11 1.8 The historical context 12 1.9 Closely related fields in Statistics 17 2 The General Linear Latent Variable Model 19 2.1 Introduction 19 2.2 The model 19 2.3 Some properties of the model 20 2.4 A special case 21 2.5 The sufficiency principle 22 2.6 Principal special cases 24 2.7 Latent variable models with non-linear terms 25 2.8 Fitting the models 27 2.9 Fitting by maximum likelihood 29 2.10 Fitting by Bayesian methods 30 2.11 Rotation 33 2.12 Interpretation 35 2.13 Sampling error of parameter estimates 38 2.14 The prior distribution 39 2.15 Posterior analysis 41 2.16 A further note on the prior 43 2.17 Psychometric Inference 44 3 The Normal Linear Factor Model 47 3.1 The model 47 3.2 Some distributional properties 48 3.3 Constraints on the model 50 3.4 Maximum likelihood estimation 50 3.5 Maximum likelihood estimation by the E-M algorithm 53 3.6 Sampling variation of estimators 55 3.7 Goodness of fit and choice of q 58 3.8 Fitting without normality assumptions: Least squares methods 59 3.9 Other methods of fitting 61 3.10 Approximate methods for estimating 62 3.11 Goodness-of-fit and choice of q for least squares methods 63 3.12 Further estimation issues 64 3.13 Rotation and related matters 69 3.14 Posterior analysis: The normal case 67 3.15 Posterior analysis: least squares 72 3.16 Posterior analysis: a reliability approach 74 3.17 Examples 74 4 Binary Data: Latent Trait Models 83 4.1 Preliminaries 83 4.2 The logit/normal model 84 4.3 The probit/normal model 86 4.4 The equivalence of the response function and underlying variable approaches 88 4.5 Fitting the logit/normal model: the E-M algorithm 90 4.6 Sampling properties of the maximum likelihood estimators 94 4.7 Approximate maximum likelihood estimators 95 4.8 Generalised least squares methods 96 4.9 Goodness of fit 97 4.10 Posterior analysis 100 4.11 Fitting the logit/normal and probit/normal models: Markov Chain Monte Carlo 102 4.12 Divergence of the estimation algorithm 109 4.13 Examples 109 5 Polytomous Data: Latent Trait Models 119 5.1 Introduction 119 5.2 A response function model based on the sufficiency principle 120 5.3 Parameter interpretation 124 5.4 Rotation 124 5.5 Maximum likelihood estimation of the polytomous logit model 125 5.6 An approximation to the likelihood 126 5.7 Binary data as a special case 134 5.8 Ordering of categories 136 5.9 An alternative underlying variable model 144 5.10 Posterior analysis 147 5.11 Further observations 148 5.12 Examples of the analysis of polytomous data using the logit model 149 6 Latent Class Models 157 6.1 Introduction 157 6.2 The latent class model with binary manifest variables 158 6.3 The latent class model for binary data as a latent trait model 159 6.4 Latent Classes within the GLLVM 161 6.5 Maximum likelihood estimation 162 6.6 Standard errors 164 6.7 Posterior analysis of the latent class model with binary manifest variables 166 6.8 Goodness of Fit 167 6.9 Examples for binary Data 167 6.10 Latent class models with unordered polytomous manifest variables 170 6.11 Latent class models with ordered polytomous manifest variables 171 6.12 Maximum likelihood estimation 172 6.13 Examples for unordered polytomous data 174 6.14 Identifiability 178 6.15 Starting values 180 6.16 Latent class models with metrical manifest variables 180 6.17 Models with ordered latent classes 181 6.18 Hybrid models 182 7 Models and Methods for Manifest Variables of Mixed Type 191 7.1 Introduction 191 7.2 Principal results 192 7.3 Other members of the exponential family 193 7.4 Maximum likelihood estimation 195 7.5 Sampling properties and Goodness of Fit 201 7.6 Mixed latent class models 202 7.7 Posterior analysis 203 7.8 Examples 204 7.9 Ordered categorical variables and other generalisations 208 8 Relationships Between Latent Variables 213 8.1 Scope 213 8.2 Correlated latent variables 213 8.3 Procrustes methods 215 8.4 Sources of prior knowledge 215 8.5 Linear structural relations models 216 8.6 The LISREL model 218 8.7 Adequacy of a structural equation model 221 8.8 Structural relationships in a general setting 222 8.9 Generalisations of the LISREL model 223 8.10 Examples of models which are indistinguishable 224 8.11 Implications for analysis 227 9 Related Techniques for Investigating Dependency 229 9.1 Introduction 229 9.2 Principal Components Analysis, (PCA) 229 9.3 An alternative to the normal factor model 236 9.4 Replacing latent variables by linear functions of the manifest variables 238 9.5 Estimation of correlations and regressions between latent variables 240 9.6 Q-Methodology 242 9.7 Concluding reflections of the role of latent variables in statistical modelling 244 References 247 Software appendix 247 References 249 Author Index 265 Subject Index 271

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Book SynopsisA practical guide for determining the evidential value of physicochemical data Microtraces of various materials (e.g. glass, paint, fibres, and petroleum products) are routinely subjected to physicochemical examination by forensic experts, whose role is to evaluate such physicochemical data in the context of the prosecution and defence propositions. Such examinations return various kinds of information, including quantitative data. From the forensic point of view, the most suitable way to evaluate evidence is the likelihood ratio. This book provides a collection of recent approaches to the determination of likelihood ratios and describes suitable software, with documentation and examples of their use in practice. The statistical computing and graphics software environment R, pre-computed Bayesian networks using Hugin Researcher and a new package, calcuLatoR, for the computation of likelihood ratios are all explored. Statistical Analysis in ForeTable of ContentsPreface xiii 1 Physicochemical data obtained in forensic science laboratories 1 1.1 Introduction 1 1.2 Glass 2 1.3 Flammable liquids: ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and inks: MSP-DAD technique 13 References 15 2 Evaluation of evidence in the form of physicochemical data 19 2.1 Introduction 19 2.2 Comparison problem 21 2.3 Classification problem 27 2.4 Likelihood ratio and Bayes’ theorem 31 References 32 3 Continuous data 35 3.1 Introduction 35 3.2 Data transformations 37 3.3 Descriptive statistics 39 3.4 Hypothesis testing 59 3.5 Analysis of variance 78 3.6 Cluster analysis 85 3.7 Dimensionality reduction 92 References 105 4 Likelihood ratio models for comparison problems 107 4.1 Introduction 107 4.2 Normal between-object distribution 108 4.3 Between-object distribution modelled by kernel density estimation 110 4.4 Examples 112 4.5 R Software 140 References 149 5 Likelihood ratio models for classification problems 151 5.1 Introduction 151 5.2 Normal between-object distribution 152 5.3 Between-object distribution modelled by kernel density estimation 155 5.4 Examples 157 5.5 R software 172 References 179 6 Performance of likelihood ratio methods 181 6.1 Introduction 181 6.2 Empirical measurement of the performance of likelihood ratios 182 6.3 Histograms and Tippett plots 183 6.4 Measuring discriminating power 186 6.5 Accuracy equals discriminating power plus calibration: Empirical cross-entropy plots 192 6.6 Comparison of the performance of different methods for LR computation 200 6.7 Conclusions: What to measure, and how 214 6.8 Software 215 References 216 Appendix A Probability 218 A.1 Laws of probability 218 A.2 Bayes’ theorem and the likelihood ratio 222 A.3 Probability distributions for discrete data 225 A.4 Probability distributions for continuous data 227 References 227 Appendix B Matrices: An introduction to matrix algebra 228 B.1 Multiplication by a constant 228 B.2 Adding matrices 229 B.3 Multiplying matrices 230 B.4 Matrix transposition 232 B.5 Determinant of a matrix 232 B.6 Matrix inversion 233 B.7 Matrix equations 235 B.8 Eigenvectors and eigenvalues 237 Reference 239 Appendix C Pool adjacent violators algorithm 240 References 243 Appendix D Introduction to R software 244 D.1 Becoming familiar with R 244 D.2 Basic mathematical operations in R 246 D.3 Data input 252 D.4 Functions in R 254 D.5 Dereferencing 255 D.6 Basic statistical functions 257 D.7 Graphics with R 258 D.8 Saving data 266 D.9 R codes used in Chapters 4 and 5 266 D.10 Evaluating the performance of LR models 289 Reference 293 Appendix E Bayesian network models 294 E.1 Introduction to Bayesian networks 294 E.2 Introduction to Hugin ResearcherTM software 296 References 308 Appendix F Introduction to calcuLatoR software 309 F.1 Introduction 309 F.2 Manual 309 Reference 314 Index 315

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Book SynopsisStatistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography.Table of ContentsPreface xi 1 Dealing with data 1 1.1 The role of statistics in geography 1 1.2 About this book 3 1.3 Data and measurement error 3 2 Collecting and summarizing data 13 2.1 Sampling methods 13 2.2 Graphicalsummaries 17 2.3 Summarizing data numerically 24 3 Probability and sampling distributions 37 3.1 Probability 37 3.2 Probability and the normal distribution: z]scores 39 3.3 Sampling distributions and the central limit theorem 43 4 Estimating parameters with confidence intervals 49 4.1 Confidence intervals on the mean of a normal distribution: the basics 49 4.2 Confidence intervals in practice: the t]distribution 50 4.3 Sample size 53 4.4 Confidence intervals for a proportion 53 5 Comparing datasets 55 5.1 Hypothesis testing with one sample: general principles 55 5.2 Comparing means from small samples: one]sample t]test 61 5.3 Comparing proportions for one sample 63 5.4 Comparing two samples 64 5.5 Non]parametric hypothesis testing 75 6 Comparing distributions: the Chi]squared test 81 6.1 Chi]squared test with one sample 81 6.2 Chi]squared test for two samples 84 7 Analysis of variance 89 7.1 Oneway analysis of variance 90 7.2 Assumptions and diagnostics 99 7.3 Multiple comparison tests after analysis of variance 101 7.4 Non]parametric methods in the analysis of variance 105 7.5 Summary and further applications 106 8 Correlation 109 8.1 Correlation analysis 109 8.2 Pearson’s product]moment correlation coefficient 110 8.3 Significance tests of correlation coefficient 112 8.4 Spearman’s rank correlation coefficient 114 8.5 Correlation and causality 116 9 Linear regression 121 9.1 Least]squares linear regression 121 9.2 Scatter plots 122 9.3 Choosing the line of best fit: the ‘least]squares’procedure 124 9.4 Analysis of residuals 128 9.5 Assumptions and caveats with regression 130 9.6 Is the regression significant? 131 9.7 Coefficient of determination 135 9.8 Confidence intervals and hypothesis tests concerning regression parameters 137 9.9 Reduced major axis regression 140 10 Spatial statistics 145 10.1 Spatial data 145 10.2 Summarizing spatial data 157 10.3 Identifying clusters 159 10.4 Interpolation and plotting contour maps 162 10.5 Spatial relationships 163 11 Time series analysis 173 11.1 Time series in geographical research 173 11.2 Analysing time series 174 Appendix A: Introduction to the R package 193 Appendix B: Statistical tables 205 References 241 Index 243

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Book SynopsisStatistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography.Table of ContentsPreface xi 1 Dealing with data 1 1.1 The role of statistics in geography 1 1.1.1 Why do geographers need to use statistics? 1 1.2 About this book 3 1.3 Data and measurement error 3 1.3.1 Types of geographical data: nominal, ordinal, interval, and ratio 3 1.3.2 Spatial data types 5 1.3.3 Measurement error, accuracy and precision 6 1.3.4 Reporting data and uncertainties 7 1.3.5 Significant figures 9 1.3.6 Scientific notation (standard form) 10 1.3.7 Calculations in scientific notation 11 Exercises 12 2 Collecting and summarizing data 13 2.1 Sampling methods 13 2.1.1 Research design 13 2.1.2 Random sampling 15 2.1.3 Systematic sampling 16 2.1.4 Stratified sampling 17 2.2 Graphical summaries 17 2.2.1 Frequency distributions and histograms 17 2.2.2 Time series plots 21 2.2.3 Scatter plots 22 2.3 Summarizing data numerically 24 2.3.1 Measures of central tendency: mean, median and mode 24 2.3.2 Mean 24 2.3.3 Median 25 2.3.4 Mode 25 2.3.5 Measures of dispersion 28 2.3.6 Variance 29 2.3.7 Standard deviation 30 2.3.8 Coefficient of variation 30 2.3.9 Skewness and kurtosis 33 Exercises 33 3 Probability and sampling distributions 37 3.1 Probability 37 3.1.1 Probability, statistics and random variables 37 3.1.2 The properties of the normal distribution 38 3.2 Probability and the normal distribution: z‐scores 39 3.3 Sampling distributions and the central limit theorem 43 Exercises 47 4 Estimating parameters with confidence intervals 49 4.1 Confidence intervals on the mean of a normal distribution: the basics 49 4.2 Confidence intervals in practice: the t‐distribution 50 4.3 Sample size 53 4.4 Confidence intervals for a proportion 53 Exercises 54 5 Comparing datasets 55 5.1 Hypothesis testing with one sample: general principles 55 5.1.1 Comparing means: one‐sample z‐test 56 5.1.2 p‐values 60 5.1.3 General procedure for hypothesis testing 61 5.2 Comparing means from small samples: one‐sample t‐test 61 5.3 Comparing proportions for one sample 63 5.4 Comparing two samples 64 5.4.1 Independent samples 64 5.4.2 Comparing means: t‐test with unknown population variances assumed equal 64 5.4.3 Comparing means: t‐test with unknown population variances assumed unequal 68 5.4.4 t‐test for use with paired samples (paired t‐test) 71 5.4.5 Comparing variances: F‐test 74 5.5 Non‐parametric hypothesis testing 75 5.5.1 Parametric and non‐parametric tests 75 5.5.2 Mann–whitney U‐test 75 Exercises 79 6 Comparing distributions: the Chi‐squared test 81 6.1 Chi‐squared test with one sample 81 6.2 Chi‐squared test for two samples 84 Exercises 87 7 Analysis of variance 89 7.1 One‐way analysis of variance 90 7.2 Assumptions and diagnostics 99 7.3 Multiple comparison tests after analysis of variance 101 7.4 Non‐parametric methods in the analysis of variance 105 7.5 Summary and further applications 106 Exercises 107 8 Correlation 109 8.1 Correlation analysis 109 8.2 Pearson’s product‐moment correlation coefficient 110 8.3 Significance tests of correlation coefficient 112 8.4 Spearman’s rank correlation coefficient 114 8.5 Correlation and causality 116 Exercises 117 9 Linear regression 121 9.1 Least‐squares linear regression 121 9.2 Scatter plots 122 9.3 Choosing the line of best fit: the ‘least‐squares’ procedure 124 9.4 Analysis of residuals 128 9.5 Assumptions and caveats with regression 130 9.6 Is the regression significant? 131 9.7 Coefficient of determination 135 9.8 Confidence intervals and hypothesis tests concerning regression parameters 137 9.8.1 Standard error of the regression parameters 137 9.8.2 Tests on the regression parameters 138 9.8.3 Confidence intervals on the regression parameters 139 9.8.4 Confidence interval about the regression line 140 9.9 Reduced major axis regression 140 9.10 Summary 142 Exercises 142 10 Spatial statistics 145 10.1 Spatial data 145 10.1.1 Types of spatial data 145 10.1.2 Spatial data structures 146 10.1.3 Map projections 149 10.2 Summarizing spatial data 157 10.2.1 Mean centre 157 10.2.2 Weighted mean centre 157 10.2.3 Density estimation 158 10.3 Identifying clusters 159 10.3.1 Quadrat test 159 10.3.2 Nearest neighbour statistics 162 10.4 Interpolation and plotting contour maps 162 10.5 Spatial relationships 163 10.5.1 Spatial autocorrelation 163 10.5.2 Join counts 164 Exercises 171 11 Time series analysis 173 11.1 Time series in geographical research 173 11.2 Analysing time series 174 11.2.1 Describing time series: definitions 174 11.2.2 Plotting time series 175 11.2.3 Decomposing time series: trends, seasonality and irregular fluctuations 179 11.2.4 Analysing trends 180 11.2.5 Removing trends (‘detrending’ data) 186 11.2.6 Quantifying seasonal variation 187 11.2.7 Autocorrelation 189 11.3 Summary 190 Exercises 190 Appendix A: Introduction to the R package 193 Appendix B: Statistical tables 205 References 241 Index 243

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Book SynopsisA guide to the validation and risk management of quantitative models used for pricing and hedging Whereas the majority of quantitative finance books focus on mathematics and risk management books focus on regulatory aspects, this book addresses the elements missed by this literature--the risks of the models themselves. This book starts from regulatory issues, but translates them into practical suggestions to reduce the likelihood of model losses, basing model risk and validation on market experience and on a wide range of real-world examples, with a high level of detail and precise operative indications.Table of ContentsPreface xi Acknowledgements xix Part I Theory and Practice of Model Risk Management 1 Understanding Model Risk 3 1.1 What Is Model Risk? 3 1.1.1 The Value Approach 4 1.1.2 The Price Approach 6 1.1.3 A Quant Story of the Crisis 9 1.1.4 A Synthetic View on Model Risk 17 1.2 Foundations of Modelling and the Reality of Markets 22 1.2.1 The Classic Framework 22 1.2.2 Uncertainty and Illiquidity 30 1.3 Accounting for Modellers 38 1.3.1 Fair Value 38 1.3.2 The Liquidity Bubble and the Accountancy Boards 40 1.3.3 Level 1, 2, 3 .go? 41 1.3.4 The Hidden Model Assumptions in ‘vanilla’ Derivatives 42 1.4 What Regulators Said After the Crisis 48 1.4.1 Basel New Principles: The Management Process 49 1.4.2 Basel New Principles: The Model, The Market and The Product 51 1.4.3 Basel New Principles: Operative Recommendations 52 1.5 Model Validation and Risk Management: Practical Steps 53 1.5.1 A Scheme for Model Validation 54 1.5.2 Special Points in Model Risk Management 59 1.5.3 The Importance of Understanding Models 60 2 Model Validation and Model Comparison: Case Studies 63 2.1 The Practical Steps of Model Comparison 63 2.2 First Example: The Models 65 2.2.1 The Credit Default Swap 66 2.2.2 Structural First-Passage Models 67 2.2.3 Reduced-Form Intensity Models 69 2.2.4 Structural vs Intensity: Information 72 2.3 First Example: The Payoff. Gap Risk in a Leveraged Note 74 2.4 The Initial Assessment 77 2.4.1 First Test: Calibration to Liquid Relevant Products 77 2.4.2 Second Test: a Minimum Level of Realism 78 2.5 The Core Risk in the Product 81 2.5.1 Structural Models: Negligible Gap Risk 82 2.5.2 Reduced-Form Models: Maximum Gap Risk 82 2.6 A Deeper Analysis: Market Consensus and Historical Evidence 85 2.6.1 What to Add to the Calibration Set 85 2.6.2 Performing Market Intelligence 86 2.6.3 The Lion and the Turtle. Incompleteness in Practice 86 2.6.4 Reality Check: Historical Evidence and Lack of it 87 2.7 Building a Parametric Family of Models 88 2.7.1 Understanding Model Implications 93 2.8 Managing Model Uncertainty: Reserves, Limits, Revisions 95 2.9 Model Comparison: Examples from Equity and Rates 99 2.9.1 Comparing Local and Stochastic Volatility Models in Pricing Equity Compound and Barrier Options 99 2.9.2 Comparing Short Rate and Market Models in Pricing Interest Rate Bermudan Options 105 3 Stress Testing and the Mistakes of the Crisis 111 3.1 Learning Stress Test from the Crisis 111 3.1.1 The Meaning of Stress Testing 112 3.1.2 Portfolio Stress Testing 113 3.1.3 Model Stress Testing 116 3.2 The Credit Market and the ‘Formula that Killed Wall Street’ 118 3.2.1 The CDO Payoff 118 3.2.2 The Copula 119 3.2.3 Applying the Copula to CDOs 122 3.2.4 The Market Quotation Standard 124 3.3 Portfolio Stress Testing and the Correlation Mistake 125 3.3.1 From Flat Correlation Towards a Realistic Approach 126 3.3.2 A Correlation Parameterization to Stress the Market Skew 131 3.4 Payoff Stress and the Liquidity Mistake 136 3.4.1 Detecting the Problem: Losses Concentrated in Time 137 3.4.2 The Problem in Practice 139 3.4.3 A Solution. From Copulas to Real Models 145 3.4.4 Conclusions 150 3.5 Testing with Historical Scenarios and the Concentration Mistake 151 3.5.1 The Mapping Methods for Bespoke Portfolios 152 3.5.2 The Lehman Test 156 3.5.3 Historical Scenarios to Test Mapping Methods 157 3.5.4 The Limits of Mapping and the Management of Model Risk 164 3.5.5 Conclusions 168 4 Preparing for Model Change. Rates and Funding in the New Era 171 4.1 Explaining the Puzzle in the Interest Rates Market and Models 171 4.1.1 The Death of a Market Model: 9 August 2007 173 4.1.2 Finding the New Market Model 174 4.1.3 The Classic Risk-free Market Model 178 4.1.4 A Market Model with Stable Default Risk 182 4.1.5 A Market with Volatile Credit Risk 192 4.1.6 Conclusions 200 4.2 Rethinking the Value of Money: The Effect of Liquidity in Pricing 201 4.2.1 The Setting 204 4.2.2 Standard DVA: Is Something Missing? 206 4.2.3 Standard DVA plus Liquidity: Is Something Duplicated? 207 4.2.4 Solving the Puzzle 207 4.2.5 Risky Funding for the Borrower 208 4.2.6 Risky Funding for the Lender and the Conditions for Market Agreement 209 4.2.7 Positive Recovery Extension 210 4.2.8 Two Ways of Looking at the Problem: Default Risk or Funding Benefit? The Accountant vs the Salesman 211 4.2.9 Which Direction for Future Pricing? 214 Part II Snakes in the Grass: Where Model Risk Hides 5 Hedging 219 5.1 Model Risk and Hedging 219 5.2 Hedging and Model Validation: What is Explained by P&L Explain? 221 5.2.1 The Sceptical View 222 5.2.2 The Fundamentalist View and Black and Scholes 222 5.2.3 Back to Reality 224 5.2.4 Remarks: Recalibration, Hedges and Model Instability 226 5.2.5 Conclusions: from Black and Scholes to Real Hedging 228 5.3 From Theory to Practice: Real Hedging 229 5.3.1 Stochastic Volatility Models: SABR 231 5.3.2 Test Hedging Behaviour Leaving Nothing Out 232 5.3.3 Real Hedging for Local Volatility Models 238 5.3.4 Conclusions: the Reality of Hedging Strategies 241 6 Approximations 243 6.1 Validate and Monitor the Risk of Approximations 243 6.2 The Swaption Approximation in the Libor Market Model 245 6.2.1 The Three Technical Problems in Interest Rate Modelling 245 6.2.2 The Libor Market Model and the Swaption Market 247 6.2.3 Pricing Swaptions 250 6.2.4 Understanding and Deriving the Approximation 253 6.2.5 Testing the Approximation 257 6.3 Approximations for CMS and the Shape of the Term Structure 264 6.3.1 The CMS Payoff 265 6.3.2 Understanding Convexity Adjustments 266 6.3.3 The Market Approximation for Convexity Adjustments 267 6.3.4 A General LMM Approximation 269 6.3.5 Comparing and Testing the Approximations 271 6.4 Testing Approximations Against Exact. Dupire’s Idea 276 6.4.1 Perfect Positive Correlation 278 6.4.2 Perfect Negative Correlation 280 6.5 Exercises on Risk in Computational Methods 283 6.5.1 Approximation 283 6.5.2 Integration 285 6.5.3 Monte Carlo 285 7 Extrapolations 287 7.1 Using the Market to Complete Information: Asymptotic Smile 288 7.1.1 The Indetermination in the Asymptotic Smile 288 7.1.2 Pricing CMS with a Smile: Extrapolating to Infinity 292 7.1.3 Using CMS Information to Transform Extrapolation into Interpolation and Fix the Indetermination 293 7.2 Using Mathematics to Complete Information: Correlation Skew 295 7.2.1 The Expected Tranched Loss 295 7.2.2 Properties for Interpolation 298 7.2.3 Properties for Turning Extrapolation into Interpolation 298 8 Correlations 303 8.1 The Technical Difficulties in Computing Correlations 303 8.1.1 Correlations in Interest Rate Modelling 305 8.1.2 Cross-currency Correlations 307 8.1.3 Stochastic Volatility Correlations 312 8.2 Fundamental Errors in Modelling Correlations 315 8.2.1 The Zero-correlation Error 316 8.2.2 The 1-Correlation Error 319 9 Calibration 323 9.1 Calibrating to Caps/Swaptions and Pricing Bermudans 324 9.1.1 Calibrating Caplets 325 9.1.2 Understanding the Term Structure of Volatility 326 9.1.3 Different Parameterizations 329 9.1.4 The Evolution of the Term Structure of Volatility 332 9.1.5 The Effect on Early-Exercise Derivatives 334 9.1.6 Reducing Our Indetermination in Pricing Bermudans: Liquid European Swaptions 335 9.2 The Evolution of the Forward Smiles 340 10 When the Payoff is Wrong 347 10.1 The Link Between Model Errors and Payoff Errors 347 10.2 The Right Payoff at Default: The Impact of the Closeout Convention 348 10.2.1 How Much Will be Paid at Closeout, Really? 350 10.2.2 What the Market Says and What the ISDA Says 352 10.2.3 A Quantitative Analysis of the Closeout 353 10.2.4 A Summary of the Findings and Some Conclusions on Payoff Uncertainty 360 10.3 Mathematical Errors in the Payoff of Index Options 362 10.3.1 Too Much Left Out 364 10.3.2 Too Much Left In 365 10.3.3 Empirical Results with the Armageddon Formula 365 10.3.4 Payoff Errors and Armageddon Probability 367 11 Model Arbitrage 371 11.1 Introduction 371 11.2 Capital Structure Arbitrage 373 11.2.1 The Credit Model 373 11.2.2 The Equity Model 375 11.2.3 From Barrier Options to Equity Pricing 377 11.2.4 Capital-structure Arbitrage and Uncertainty 381 11.3 The Cap-Swaption Arbitrage 391 11.4 Conclusion: Can We Use No-Arbitrage Models to Make Arbitrage? 394 12 Appendix 397 12.1 Random Variables 397 12.1.1 Generating Variables from Uniform Draws 397 12.1.2 Copulas 397 12.1.3 Normal and Lognormal 398 12.2 Stochastic Processes 399 12.2.1 The Law of Iterated Expectation 399 12.2.2 Diffusions, Brownian Motions and Martingales 400 12.2.3 Poisson Process 403 12.2.4 Time-dependent Intensity 404 12.3 Useful Results from Quantitative Finance 405 12.3.1 Black and Scholes (1973) and Black (1976) 405 12.3.2 Change of Numeraire 407 Bibliography 409 Index 417

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Book SynopsisThis is the only major text in the area of chemometrics published over the last decade focusing exclusively on pattern recognition. The coverage uses real world pattern recognition case studies, often involving quite large and complex datasets.Table of ContentsAcknowledgements. Preface. 1 Introduction. 1.1 Past, Present and Future. 1.2 About this Book. Bibliography. 2 Case Studies. 2.1 Introduction. 2.2 Datasets, Matrices and Vectors. 2.3 Case Study 1: Forensic Analysis of Banknotes. 2.4 Case Study 2: Near Infrared Spectroscopic Analysis of Food. 2.5 Case Study 3: Thermal Analysis of Polymers. 2.6 Case Study 4: Environmental Pollution using Headspace Mass Spectrometry. 2.7 Case Study 5: Human Sweat Analysed by Gas Chromatography Mass Spectrometry. 2.8 Case Study 6: Liquid Chromatography Mass Spectrometry of Pharmaceutical Tablets. 2.9 Case Study 7: Atomic Spectroscopy for the Study of Hypertension. 2.10 Case Study 8: Metabolic Profiling of Mouse Urine by Gas Chromatography of Urine Extracts. 2.11 Case Study 9: Nuclear Magnetic Resonance Spectroscopy for Salival Analysis of the Effect of Mouthwash. 2.12 Case Study 10: Simulations. 2.13 Case Study 11: Null Dataset. 2.14 Case Study 12: GCMS and Microbiology of Mouse Scent Marks. Bibliography. 3 Exploratory Data Analysis. 3.1 Introduction. 3.2 Principal Components Analysis. 3.2.1 Background. 3.2.2 Scores and Loadings. 3.2.3 Eigenvalues. 3.2.4 PCA Algorithm. 3.2.5 Graphical Representation. 3.3 Dissimilarity Indices, Principal Co-ordinates Analysis and Ranking. 3.3.1 Dissimilarity. 3.3.2 Principal Co-ordinates Analysis. 3.3.3 Ranking. 3.4 Self Organizing Maps. 3.4.1 Background. 3.4.2 SOM Algorithm. 3.4.3 Initialization. 3.4.4 Training. 3.4.5 Map Quality. 3.4.6 Visualization. Bibliography. 4 Preprocessing. 4.1 Introduction. 4.2 Data Scaling. 4.2.1 Transforming Individual Elements. 4.2.2 Row Scaling. 4.2.3 Column Scaling. 4.3 Multivariate Methods of Data Reduction. 4.3.1 Largest Principal Components. 4.3.2 Discriminatory Principal Components. 4.3.3 Partial Least Squares Discriminatory Analysis Scores. 4.4 Strategies for Data Preprocessing. 4.4.1 Flow Charts. 4.4.2 Level 1. 4.4.3 Level 2. 4.4.4 Level 3. 4.4.5 Level 4. Bibliography. 5 Two Class Classifiers. 5.1 Introduction. 5.1.1 Two Class Classifiers. 5.1.2 Preprocessing. 5.1.3 Notation. 5.1.4 Autoprediction and Class Boundaries. 5.2 Euclidean Distance to Centroids. 5.3 Linear Discriminant Analysis. 5.4 Quadratic Discriminant Analysis. 5.5 Partial Least Squares Discriminant Analysis. 5.5.1 PLS Method. 5.5.2 PLS Algorithm. 5.5.3 PLS-DA. 5.6 Learning Vector Quantization. 5.6.1 Voronoi Tesselation and Codebooks. 5.6.2 LVQ1. 5.6.3 LVQ3. 5.6.4 LVQ Illustration and Summary of Parameters. 5.7 Support Vector Machines. 5.7.1 Linear Learning Machines. 5.7.2 Kernels. 5.7.3 Controlling Complexity and Soft Margin SVMs. 5.7.4 SVM Parameters. Bibliography. 6 One Class Classifiers. 6.1 Introduction. 6.2 Distance Based Classifiers. 6.3 PC Based Models and SIMCA. 6.4 Indicators of Significance. 6.4.1 Gaussian Density Estimators and Chi-Squared. 6.4.2 Hotelling’s T2. 6.4.3 D-Statistic. 6.4.4 Q-Statistic or Squared Prediction Error. 6.4.5 Visualization of D- and Q-Statistics for Disjoint PC Models. 6.4.6 Multivariate Normality and What to do if it Fails. 6.5 Support Vector Data Description. 6.6 Summarizing One Class Classifiers. 6.6.1 Class Membership Plots. 6.6.2 ROC Curves. Bibliography. 7 Multiclass Classifiers. 7.1 Introduction. 7.2 EDC, LDA and QDA. 7.3 LVQ. 7.4 PLS. 7.4.1 PLS2. 7.4.2 PLS1. 7.5 SVM. 7.6 One against One Decisions. Bibliography. 8 Validation and Optimization. 8.1 Introduction. 8.1.1 Validation. 8.1.2 Optimization. 8.2 Classification Abilities, Contingency Tables and Related Concepts. 8.2.1 Two Class Classifiers. 8.2.2 Multiclass Classifiers. 8.2.3 One Class Classifiers. 8.3 Validation. 8.3.1 Testing Models. 8.3.2 Test and Training Sets. 8.3.3 Predictions. 8.3.4 Increasing the Number of Variables for the Classifier. 8.4 Iterative Approaches for Validation. 8.4.1 Predictive Ability, Model Stability, Classification by Majority Vote and Cross Classification Rate. 8.4.2 Number of Iterations. 8.4.3 Test and Training Set Boundaries. 8.5 Optimizing PLS Models. 8.5.1 Number of Components: Cross-Validation and Bootstrap. 8.5.2 Thresholds and ROC Curves. 8.6 Optimizing Learning Vector Quantization Models. 8.7 Optimizing Support Vector Machine Models. Bibliography. 9 Determining Potential Discriminatory Variables. 9.1 Introduction. 9.1.1 Two Class Distributions. 9.1.2 Multiclass Distributions. 9.1.3 Multilevel and Multiway Distributions. 9.1.4 Sample Sizes. 9.1.5 Modelling after Variable Reduction. 9.1.6 Preliminary Variable Reduction. 9.2 Which Variables are most Significant?. 9.2.1 Basic Concepts: Statistical Indicators and Rank. 9.2.2 T-Statistic and Fisher Weights. 9.2.3 Multiple Linear Regression, ANOVA and the F-Ratio. 9.2.4 Partial Least Squares. 9.2.5 Relationship between the Indicator Functions. 9.3 How Many Variables are Significant? 9.3.1 Probabilistic Approaches. 9.3.2 Empirical Methods: Monte Carlo. 9.3.3 Cost/Benefit of Increasing the Number of Variables. Bibliography. 10 Bayesian Methods and Unequal Class Sizes. 10.1 Introduction. 10.2 Contingency Tables and Bayes’ Theorem. 10.3 Bayesian Extensions to Classifiers. Bibliography. 11 Class Separation Indices. 11.1 Introduction. 11.2 Davies Bouldin Index. 11.3 Silhouette Width and Modified Silhouette Width. 11.3.1 Silhouette Width. 11.3.2 Modified Silhouette Width. 11.4 Overlap Coefficient. Bibliography. 12 Comparing Different Patterns. 12.1 Introduction. 12.2 Correlation Based Methods. 12.2.1 Mantel Test. 12.2.2 RV Coefficient. 12.3 Consensus PCA. 12.4 Procrustes Analysis. Bibliography. Index.

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Book SynopsisFinite mixture models are typically used where the population being studied is heterogeneous in composition. This work aims to offer an up-to-date account of the major issues involved with finite modelling. There is a practical emphasis on the applications of mixture models.Trade Review"This is an excellent book.... I enjoyed reading this book. I recommend it highly to both mathematical and applied statisticians." (Technometrics, February 2002) "This book will become popular to many researchers...the material covered is so wide that it will make this book a standard reference for the forthcoming years." (Zentralblatt MATH, Vol. 963, 2001/13) "the material covered is so wide that it will make this book a standard reference for the forthcoming years." (Zentralblatt MATH, Vol.963, No.13, 2001) "This book is excellent reading...should also serve as an excellent handbook on mixture modelling..." (Mathematical Reviews, 2002b) "...contains valuable information about mixtures for researchers..." (Journal of Mathematical Psychology, 2002) "...a masterly overview of the area...It is difficult to ask for more and there is no doubt that McLachlan and Peel's book will be the standard reference on mixture models for many years to come." (Statistical Methods in Medical Research, Vol. 11, 2002) "...they are to be congratulated on the extent of their achievement..." (The Statistician, Vol.51, No.3)Table of ContentsGeneral Introduction. ML Fitting of Mixture Models. Multivariate Normal Mixtures. Bayesian Approach to Mixture Analysis. Mixtures with Nonnormal Components. Assessing the Number of Components in Mixture Models. Multivariate t Mixtures. Mixtures of Factor Analyzers. Fitting Mixture Models to Binned Data. Mixture Models for Failure-Time Data. Mixture Analysis of Directional Data. Variants of the EM Algorithm for Large Databases. Hidden Markov Models. Appendices. References. Indexes.

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Book SynopsisAn accessible book on sampling techniques with emphasis on and illustrations from surveys of human populations. Explains how to design and execute valid samples of moderate dimensions and difficulty, avoid selection biases and how to become more adept at evaluating sample results, judge their validity and limits of inference, applicability and precision. Contains numerous practical procedures, the domestic arts of sampling along with its science plus invaluable tricks that are usually learned only in apprenticeship.Table of ContentsFUNDAMENTALS OF SURVEY SAMPLING. Basic Concepts of Sampling. Stratified Sampling. Systematic Sampling; Stratification Techniques. Cluster Sampling and Subsampling. Unequal Clusters. Selection with Probabilities Proportional to Size Measures(PPS). The Economic Design of Surveys. SPECIAL PROBLEMS AND TECHNIQUES. Area Sampling. Multistage Sampling. Sampling from Imperfect Frames. Some Selection Techniques. RELATED CONCEPTS. Biases and Nonsampling Errors. Some Issues of Inference from Survey Data. Appendices. References. Answers to Selected Problems. Index.

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Book SynopsisThis book contains material on compound Poisson random variables including an identity which can be used to efficiently compute moments, Poisson approximations, and coverage of the mean time spent in transient states as well as examples relating to the Gibba s sampler, the Metropolis algorithm and mean cover time in star graphs.Table of ContentsPreliminaries. The Poisson Process. Renewal Theory. Markov Chains. Continuous-Time Markov Chains. Martingales. Random Walks. Brownian Motion and Other Markov Processes. Stochastic Order Relations. Poisson Approximations. Answers and Solutions to Selected Problems. Index.

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Book SynopsisChemometrics encompasses a variety of statistical tools used to effectively design, analyze and interpret experimental data. The field has evolved rapidly over the last several years due to the widespread availability of powerful, inexpensive computers and available software packages.Trade Review"...probably the best introductory text that I have read on the subject...I would recommend this book for an introductory course...whithout the slightest hesitation." (Microchemical Journal, Vol. 69, 2001)Table of ContentsThe Six Habits of an Effective Chemometrician. Defining the Problem. Preprocessing. Pattern Recognition. Multivariate Calibration and Prediction. References. Index.

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Book SynopsisIn this text on statistical decision-making, the authors use examples such as computing values for the stock market, conducting market research reports or using an options pricing model to illuminate the subject matter.Table of ContentsIntroduction to Quantitative Decision Making. Discrete Probability and Decision Analysis. Decision Making with Binomial and Normal Probabilities. Decisions Based on Sample Statistics. Sample Design and Estimation. Decisions Based on Linear Relationships. Hypothesis Testing. Quality Control. Forecasting. Analysis of Variance. Simulation. Linear Programming. Appendices. Data Disk Files. Selected References. Answers to Even-Numbered Problems. Index.

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Book SynopsisPATTERN CLASSIFICATION a unified view of statistical and neural approaches The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable. Offering a lucid presentation of complex appTable of ContentsStatistical Decision Theory. Need for Approximations: Fundamental Approaches. Classification Based on Statistical Models Determined by First-and-Second Order Statistical Moments. Classification Based on Mean-Square Functional Approximations. Polynomial Regression. Multilayer Perceptron Regression. Radial Basis Functions. Measurements, Features, and Feature Section. Reject Criteria and Classifier Performance. Combining Classifiers. Conclusion. STATMOD Program: Description of ftp Package. References. Index.

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Book SynopsisUnlike other books on variance components, Statistical Tests for Mixed Linear Models continues beyond point estimation to cover hypothesis and data testing. By addressing these areas, the author presents practical applications of variance component models through testing of fixed effects and variance components.Trade Review"...compiles the available results in this area into a single volume." (Quarterly of Applied Mathematics, Vol. LIX, No. 3, September 2001) "...the authors are to be congratulated for this important and useful book...the authors state...'it will contribute to the development of the area, and enhance its exposure and usefulness.' The reviewers agree." (Mathematical Geology)Table of ContentsNature of Exact and Optimum Tests in Mixed Linear Models. Balanced Random and Mixed Models. Measures of Data Imbalance. Unbalanced One-Way and Two-Way Random Models. Random Models with Unequal Cell Frequencies in the Last Stage. Tests in Unbalanced Mixed Models. Recovery of Inter-Block Information. Split-Plot Designs Under Mixed and Random Models. Tests Using Generalized P-Values. Multivariate Mixed and Random Models. Appendix. General Bibliography. Indexes.

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Book SynopsisThis book examines the solution of some of the most common problems of numerical computation. By concentrating on one effective algorithm for each basic task, it develops the fundamental theory in a brief, elementary way. There are ample exercises, and codes are provided to reduce the time otherwise required for programming and debugging.Table of ContentsErrors and Floating Point Arithmetic. Systems of Linear Equations. Interpolation. Roots of Nonlinear Equations. Numerical Integration. Ordinary Differential Equations. Appendix. Answers to Selected Exercises. Index.

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Book SynopsisMaster the fundamental math skills necessary to quantify andevaluate a broad range of environmental questions. Environmental issues are often quantitative--how much land, howmany people, what amount of pollution. Computer programs areuseful, but there is no substitute for being able to use a simplecalculation to slice through to the crux of the problem. Having agrasp of how the factors interact and whether the results makesense allows one to explain and argue a point of view forcefully todiverse audiences. With an engaging, down-to-earth style and practical problem-solvingapproach, Ecological Numeracy makes it easy to understand andmaster basic mathematical concepts and techniques that areapplicable to life-cycle assessment, energy consumption, land use,pollution generation, and a broad range of other environmentalissues. Robert Herendeen brings the numbers to life with dozens offascinating, often entertaining examples and problems. Requiring only a moderate Table of ContentsContext and Acclimatization. Contributions to Environmental Impact: Analyzing the Components ofChange. Consequences of Exponential (Geometric) Growth. End-Use Analysis and Predicting Future Demand. Economic Considerations, Discount Rates, and Benefit-CostAnalysis. Limits. Dynamics, Stocks and Flows, Age Class Effects. Indirect Effects. Shared Resources and the Tragedy of the Commons. The Automobile: A Powerful Problem. Ecological Economics and Sustainability. Thermodynamics and Energy Efficiency. Appendices. References. Index.

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Book SynopsisThis text provides the reader with a general and widely-applicable problem solving strategy for use in quality improvement. It covers a variety of statistical and "non-statistical" problem-solving tools, and discusses techniques that are useful when problems are solved by groups or teams of people.Table of ContentsIntroduction Detecting and Prioritizing Problems Problem-Solving Strategies Group-Based Problem Solving The Reward Structure: The Human Side of Problem Solving Measurements and Their Importance for Quality Analysis of Information: Graphical Displays and Numerical Summaries Modeling Variability: An Introduction to Probability Distributions Sample Surveys Statistical Inference Under Simple Random Sampling Acceptance Sampling Plans Statistical Process Control: Control Charts Process Capability and PRE-Control Principles of Effective Experimental Design Analysis of Data from Effective Experimental Designs and an Introduction to Factorial Experiments Taguchi Design Methods for Product and Process Improvement Regression Analysis: A Useful Tool for Modeling Relationships

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Book SynopsisThis book concentrates on a variety of multivariate distributional models (other than the normal and related sampling distributions). It covers a wide range of models from multivariate (MV) exponential, MV extremevalue and MV gamma, to MV beta (or dirichlet) and MV pareto, to name but a few.Trade ReviewThis book brings one right up to date and is a worthy addition to the existing set of second editions of the other volumes of Distributions in Statistics. It will remain the key reference for many years. (Short Book Reviews, Vol. 20, No. 3, December 2000) [...] Continuous Multivariate Distributions is a unique and valuable source of information on multivariate distributions. This book, and the rest of this venerable and important series, should be on the shelves of every statistician. (JASA June 2001) For certain it will serve as the primary source for continuous multivariate statistical distributions for a long time. (Zentralblatt Math, Volume 946, No 21, 2000) "...provides a remarkably comprehensive, self-contained resource for this important statistical area." (Mathematical Reviews, Issue 2001h) "It will remain the key reference for many years." (Short Book Reviews, December 2000) "...will serve as the primary source for continuous multivariate statistical distributions for a long time." (Zentralblatt MATH, Vol. 946, No. 21) "Like its predecessors, this monograph is a most welcome addition to the statistical literature. We are looking forward to Volume 2..." (Statistical Papers, Vol. 42, No. 3, 2001)Table of ContentsSystems of Continuous Multivariate Distributions. Multivariate Normal Distributions. Bivariate and Trivariate Normal Distributions. Multivariate Exponential Distributions. Multivariate Gamma Distributions. Dirichlet and Inverted Dirichlet Distributions. Multivariate Liouville Distributions. Multivariate Logistic Distributions. Multivariate Pareto Distributions. Bivariate and Multivariate Extreme Value Distributions. Natural Exponential Families. Indexes.

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Book SynopsisBringing together business and engineering to reliability analysis With manufactured products exploding in numbers and complexity, reliability studies play an increasingly critical role throughout a product's entire life cycle-from design to post-sale support.Trade ReviewThis book provides a comprehensive overview of both qualitative and quantitative aspects of reliability. Mathematical and statistical concepts related to reliability modeling and analysis are presented along with important bibliography and a listing of resources which includes journals, reliability standards, other publications, and databases. The coverage of individual topics is not always deep, but this should be a valuable reference for any engineer or statisticial working in reliability. (Short Book Reviews, Vol.20, No. 3, December 2000) "...this should be a valuable reference for any engineer or statistician working in reliability." (Short Book Reviews, Vol. 20, No. 3, December 2000) This book presents a remarkably broad framework for the analysis of the technical and commercial aspects of product reliability.... Written by two highly respected experts in the field, this practical work provides engineers, operations managers, and applied statisticians with both qualitative and quantitative tools for solving a variety of complex, real-world reliability problems. (Zentralblatt Math, Volume 945, No 20, 2000) "...a comprehensive overview..." (Short Book Reviews, December 2000) "...an excellent textbook for an advanced course in biostatistics and also an indispensable reference for biostatisticians and epidemiologists" (Short Book Reviews, December 2000) "...an excellent book, distinguished by excellence of exposition, breadth and comprehensiveness of topical overage, relevance, number and importance of examples, depth of references, and quality of exercises...will become one of the standard references on reliability...so comprehensive...it would provide material for more than a two-semester graduate sequence...will find a warm welcome in the best graduate programs. I strongly recommend it." (Technometrics, Vol. 43, No. 4, November 2001) "I would recommend this book to practitioners and as a graduate level book." (Journal of the American Statistical Association, December 2001)Table of ContentsCONTEXT OF RELIABILITY ANALYSIS. An Overview. Illustrative Cases and Data Sets. BASIC RELIABILITY METHODOLOGY. Collection and Preliminary Analysis of Failure Data. Probability Distributions for Modeling Time to Failure. Basic Statistical Methods for Data Analysis. RELIABILITY MODELING, ESTIMATION, AND PREDICTION. Modeling Failures at the Component Level. Modeling and Analysis of Multicomponent Systems. Advanced Statistical Methods for Data Analysis. Software Reliability. Design of Experiments and Analysis of Variance. Model Selection and Validation. RELIABILITY MANAGEMENT, IMPROVEMENT, AND OPTIMIZATION. Reliability Management. Reliability Engineering. Reliability Prediction and Assessment. Reliability Improvement. Maintenance of Unreliable Systems. Warranties and Service Contracts. Reliability Optimization. EPILOGUE. Case Studies. Resource Materials. Appendices. References. Indexes.

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Book SynopsisAn exploration of regression graphics through computer graphics. Recent developments in computer technology have stimulated new and exciting uses for graphics in statistical analyses. Regression Graphics, one of the first graduate-level textbooks on the subject, demonstrates how statisticians, both theoretical and applied, can use these exciting innovations. After developing a relatively new regression context that requires few scope-limiting conditions, Regression Graphics guides readers through the process of analyzing regressions graphically and assessing and selecting models. This innovative reference makes use of a wide range of graphical tools, including 2D and 3D scatterplots, 3D binary response plots, and scatterplot matrices. Supplemented by a companion ftp site, it features numerous data sets and applied examples that are used to elucidate the theory. Other important features of this book include: * Extensive coverage of a relatively new regression conteTrade ReviewIn summary, it is a very well-written book with a good blend of theory and application. Some of the chapters in the book are very theoretical and very extensive so that a researcher in this area will benefit much from reading this book. Practitioners will also benefit by getting the basic ideas of the various concepts and understanding them through the abundant number of examples in the book. (Statistical Methods in Medical Research, 9: 602-604, 2000)Table of ContentsIntroduction to 2D Scatterplots. Constructing 3D Scatterplots. Interpreting 3D Scatterplots. Binary Response Variables. Dimension-Reduction Subspaces. Graphical Regression. Getting Numerical Help. Graphical Regression Studies. Inverse Regression Graphics. Sliced Inverse Regression. Principles Hessian Directions. Studying Predictor Effects. Predictor Transformations. Graphics for Model Assessment. Bibliography. Indexes.

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Book SynopsisOther volumes in the Wiley Series in Probability and MathematicalStatistics, Ralph A. Bradley, J. Stuart Hunter, David G. Kendall,& Geoffrey S. Watson, Advisory Editors Statistical Models inApplied Science Karl V. Bury Of direct interest to engineers andapplied scientists, this book presents general principles ofstatistics and specific distribution methods and models. Prominentdistribution properties and methods that are useful over a widerange of applications are covered in detail. The strengths andweaknesses of the distributional models are fully described, givingthe reader a firm, intuitive approach to the selection of the modelmost appropriate to the problem at hand. 1975 656 pp. FittingEquations To Data Computer Analysis of Multifactor Data forScientists and Engineers Cuthbert Daniel & Fred S. Wood Withthe assistance of John W. Gorman The purpose of this book is tohelp the serious data analyst, scientist, or engineer with acomputer to: recognize the strengths and limitations of hiTable of ContentsIntroduction. Simple Comparison Experiments. Two Factors, Each at Two Levels. Two Factors, Each at Three Levels. Unreplicated Three-Factor, Two-Level Experiments. Unreplicated Four-Factor, Two-Level Experiments. Three Five-Factor, Two-Level Unreplicated Experiments. Larger Two-Way Layouts. The Size of Industrial Experiments. Blocking Factorial Experiments, FractionalReplication--Elementary. Fractional Replication--Intermediate. Incomplete Factorials. Sequences of Fractional Replicates. Trend-Robust Plans. Nested Designs. Conclusions and Apologies.

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Book SynopsisA guide to achieving business successes through statistical methods Statistical methods are a key ingredient in providing data-based guidance to research and development as well as to manufacturing. Understanding the concepts and specific steps involved in each statistical method is critical for achieving consistent and on-target performance.Trade Review"I highly recommend this book to anyone interested in applying statistics to solve problems." (Journal of Food Quality, October 2004) "…an interesting collection of material in nice summary form…" (Journal of the American Statistical Association, December 2004) "Overall, Statistical Methods for Six Sigma in R & D and Manufacturingoffers some good insights and practical views of the statistical concepts covered." (Technometrics, August 2004, Vol. 46, No. 3) "...covers a large number of useful statistical methods compactly...contains a wealth of case studies and examples..." (Food Trade Review, May 2004) “...can be used as a reference or as a self-study...also as a textbook for an engineering statistics course...recommended...” (E-Streams, Vol. 7, No. 3)Table of Contents1. Introduction. 2. Basic Statistics. 2.1 Descriptive Statistics. 2.2 Statistical Distributions. 2.3 Confidence Intervals. 2.4 Sample Size. 2.5 Tolerance Intervals. 2.6 Normality, Independence and Homoscedasticity. 3. Comparative Experiments and Regression Analysis. 3.1 Hypothesis Testing Framework. 3.2 Comparing Single Population. 3.3 Comparing Two Populations. 3.4 Comparing Multiple Populations. 3.5 Correlation. 3.6 Regression Analysis. 4. Control Charts. 4.1 Role of Control Charts. 4.2 Logic of Control Limits. 4.3 Variable Control Charts. 4.4 Attribute Control Charts. 4.5 Interpreting Control Charts. 4.6 Key Success Factors. 5. Process Capability. 5.1 Capability and Performance Indices. 5.2 E stimating Capability and Performance Indices. 5.3 Six-Sigma Goal. 5.4 Planning for Improvement. 6. Other Useful Charts. 6.1 Risk-based Control Ch arts. 6.2 Modified Control Limit Chart. 6.3 Moving Average Control Chart. 6.4 Short Run Control Charts 6.5 Charts for Non-Normal Distributions. 7. Variance Components Analysis. 7.1 Chart (Random Factor). 7.2 One-way Classification (Fixed Factor). 7.3 Structured Studies and Variance Components. 8. Quality Planning with Variance Components. 8.1 Typical Manufacturing Application. 8.2 Economic Loss Functions. 8.3 Planning for Quality Improvement. 8.4 Application to Multi-Lane Manufacturing Process. 8.5 Variance Transmission Analysis. 8.6 Application to a Factorial Design. 8.7 Variance Components and Specifications. 9. Measurement Systems Analysis. 9.1 Statistical Properties of Measurement Systems. 9.2 Acceptance Criteria. 9.3 Calibration Study. 9.4 Stability and Bias Study. 9.5 Repeatability and Reproducibility (R&R) Study. 9.6 Robustness and Intermediate Precision Studies. 9.7 Linearity Study. 9.8 Method Transfer Study. 9.9 Calculating Significant Figures. 10. What Color is Your Belt? 10.1 Test. 10.2 Answers. Appendix A: Tail Area of Unit Normal Distribution. Appendix B: Probability Points of the t Distribution with v Degrees of Freedom. Appendix C: Probability Points of the x2 Distribution with v Degrees of Freedom. Appendix D1.k Values for Two-Sided Normal Tolerance Limits. Appendix D2.k Values for One-Sided Normal Tolerance Limits. Appendix E1: Percentage Points of the F Distribution: Upper 5% Points. Appendix E2: Percentage Points of the F Distribution: Upper 2.5% Points. Appendix F: Critical Values of Hartley's Maximum F Ratio Test for Homogeneity of Variances. Appendix G: Table of Control Chart Constants. Glossary Of Symbols. References. Index.

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Book SynopsisPresenting information on logistic regression models, this work explains difficult concepts through illustrative examples. This is a solutions manual to accompany applied Logistic Regression, 2nd Edition.Table of ContentsIntroduction to the Logistic Regression Model. The Multiple Logistic Regression Model. Interpretation of the Coefficients of the Logistic Regression Model. Model-Building Strategies and Methods for Logistic Regression. Assessing the Fit of the Model. Application of Logistic Regression with Different Sampling Models. Logistic Regression for Matched Case-Control Studies. Special Topics. References. Index.

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Book SynopsisThe outgrowth of more than 40 years of experience teaching and consulting with students and active researchers in many disciplines, this is a useful guide for both students and active researchers to experimental design.Trade Review"…an excellent reference for statisticians and practitioners who would like to gain broad exposure to the tools available for studying relationships between qualitative and quantitative factors…" (Journal of the American Statistical Association, June 2005) “The level of detail is higher than in most other books on similar topics and therefore makes this one a useful reference tool.” (Short Book Reviews, Vol.25, No.1, April 2005) "I will instruct statistician reporting to me to get a copy of the book, and will keep the review copy readily available on my shelf…" (Technometrics, February 2005) "There is a moderate amount of material that is not in other design books…in addition to some tricks of the trade that appear to be new…practitioners…will find the book useful." (Journal of Quality Technology, October 2004) "...an excellent resource handbook for researchers and statisticians, providing them with the tools necessary to construct better experiments and plan more efficient investigations.” (CHOICE, October 2004)Table of ContentsPreface. Introduction. The Completely Randomized Design. Linear Models for Designed Experiments. Testing Hypotheses and Determining Sample Size. Methods of Reducing Unexplained Variation. Latin Squares. Split-Plot and Related Designs. Incomplete Block Designs. Repeated Teatments Designs. Factorial Experiments, the 2n System. Factorial Experiments, the 3n System. Analysis of Experiments Without Designed Error Terms. Confounding Effects with Blocks. Fractional Factorial Experiments. Response Surface Designs. Plackett-Burmann Hadamard Plans. The General Pn and Nonstandard Factorials. Factorial Experiments with Quantitative Factors. Plans for Which Run Order is Important. Supersaturated Plans. Sequences of Fractions of Factorials. Multi-Stage xperiments. Orthogonal Arrays and Related Structures. Factorial Plans Derived via Orthogonal Arrays. Experiments on the Computer.

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Book SynopsisThis concise summary of the statistical methods used in the analysis of survival data with censoring emphasizes recently developed nonparametric techniques; outlines methods in detail and illustrates them with actual data; discusses the theory behind each method; and includes numerous worked problems and numerical exercises.Table of ContentsIntroduction to Survival Concepts. Parametric Models. Nonparametric Methods: One Sample. Nonparametric Methods: Two Samples. Nonparametric Methods: K Samples. Nonparametric Methods: Regression. Goodness of Fit. Miscellaneous Topics. Problems. References. Index.

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Book SynopsisThis book is about the philosophy and practice of Evolutionary Operation (called EVOP for short), a simple but powerful statistical tool with wide application in industry.Table of ContentsThe Basic Ideas. Simple Statistical Principles on Which EVOP is Based. The 2^2 and 2^3 Factorial Designs. Worksheets for Two-Variable EVOP Programs. Worksheets for Three-Variable EVOP Programs. Some Aspects of the Organization of Evolutionary Operation. EVOP, Optimization, and Variations of EVOP. Comments and Questions on EVOP. Appendices. Tables. References and Bibliography. Index.

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Book SynopsisA complete guide to the theory and practical applications of probability theory An Introduction to Probability Theory and Its Applications uniquely blends a comprehensive overview of probability theory with the real-world application of that theory. Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book''s comprehensive approach provides a complete view of theory along with enlightening examples along the way.Table of ContentsIntroduction: The Nature of Probability Theory. The Sample Space. Elements of Combinatorial Analysis. Fluctuations in Coin Tossing and Random Walks. Combination of Events. Conditional Probability. Stochastic Independence. The Binomial and Poisson Distributions. The Normal Approximation to the Binomial Distribution. Unlimited Sequences of Bernoulli Trials. Random Variables; Expectation. Laws of Large Numbers. Integral Valued Variables. Generating Functions. Compound Distributions. Branching Processes. Recurrent Events. Renewal Theory. Random Walk and Ruin Problems. Markov Chains. Algebraic Treatment of Finite Markov Chains. The Simplest Time-Dependent Stochastic Processes. Answers to Problems.

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Book SynopsisA unique introduction to the innovative methodology of statistical flowgraphs This book offers a practical, application-based approach to flowgraph models for time-to-event data. It clearly shows how this innovative new methodology can be used to analyze data from semi-Markov processes without prior knowledge of stochastic processes--opening the door to interesting applications in survival analysis and reliability as well as stochastic processes. Unlike other books on multistate time-to-event data, this work emphasizes reliability and not just biostatistics, illustrating each method with medical and engineering examples. It demonstrates how flowgraphs bring together applied probability techniques and combine them with data analysis and statistical methods to answer questions of practical interest. Bayesian methods of data analysis are emphasized. Coverage includes: * Clear instructions on how to model multistate time-to-event data using flowgraph models * An empTrade Review"…this is a well-written book on a novel and interesting approach to multistate modeling." (Biometrics, September 2006) "This book is one that researchers interested in techniques for multistate models, either in reliability or biometry should look at." (Journal of the American Statistical Association, September 2006) "…a real addition to the toolbox of both biostatisticians who use survival analysis and reliability engineers who do failure analysis on a regular basis." (Technometrics, February 2006) “…illustrated with interesting examples…the book is particularly welcome…” (International Statistical Institute, January 2006) "...a useful...account of the use of flowgraphy or semi-Markov parametric models in both industrial and biological applications." (Journal of Biopharmaceutical Statistics, September/October 2005) "Methods are explained comprehensively, with extensive examples…data analysts would find valuable examples here for their own applications." (Computing Reviews.com, June 2, 2005) “Fruitful medical and engineering examples and applications are presented…” (Zentralblatt Math, Vol.1055, No.06, 2005)Table of ContentsPreface. 1. Multistate Models and Flowgraph Models. 2. Flowgraph Models. 3. Inversion of Flowgraph Moment Generating Functions. 4. Censored Data Histograms. 5. Bayesian Prediction for Flowgraph Models. 6. Computation Implementation of Flowgraph Models. 7. Semi-Markov Processes. 8. Incomplete Data. 9. Flowgraph Models for Queuing Systems. Appendix: Moment Generating Functions. References. Author Index. Subject Index.

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Book SynopsisThis Set Contains: Continuous Multivariate Distributions, Volume 1, Models and Applications, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 1, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 2, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discrete Multivariate Distributions by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Univariate Discrete Distributions, 3rd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discover the latest advances in discrete distributions theory The Third Edition of the critically acclaimed Univariate Discrete Distributions provides a self-contained, systematic treatment of the theory, derivation, and application of probability distributions for count data. Generalized zeta-function and q-series distributTrade Review“With its thorough coverage and balanced presentation of theory and application, this is an excellent and essential reference for statisticians and mathematicians.” (Xolosepo, 27 October 2012) "The authors continue to do a praise-worthy job of making the material accessible in the third edition. This book should be on every library's shelf." (Journal of the American Statistical Association, September 2006) "These authors have achieved considerable renown for their comprehensive books on statistical distributions." (Technometrics, August 2006) "Encyclopedic in nature, the book continues to be a valuable reference." (Mathematical Reviews, 2006d) "This is an important book that should be part of every statistician's library." (MAA Reviews, January 2, 2006)Table of ContentsPreface xvii 1 Preliminary Information 1 1.1 Mathematical Preliminaries 1 1.1.1 Factorial and Combinatorial Conventions 1 1.1.2 Gamma and Beta Functions 5 1.1.3 Finite Difference Calculus 10 1.1.4 Differential Calculus 14 1.1.5 Incomplete Gamma and Beta Functions and Other Gamma-Related Functions 16 1.1.6 Gaussian Hypergeometric Functions 20 1.1.7 Confluent Hypergeometric Functions (Kummer’s Functions) 23 1.1.8 Generalized Hypergeometric Functions 26 1.1.9 Bernoulli and Euler Numbers and Polynomials 29 1.1.10 Integral Transforms 32 1.1.11 Orthogonal Polynomials 32 1.1.12 Basic Hypergeometric Series 34 1.2 Probability and Statistical Preliminaries 37 1.2.1 Calculus of Probabilities 37 1.2.2 Bayes’s Theorem 41 1.2.3 Random Variables 43 1.2.4 Survival Concepts 45 1.2.5 Expected Values 47 1.2.6 Inequalities 49 1.2.7 Moments and Moment Generating Functions 50 1.2.8 Cumulants and Cumulant Generating Functions 54 1.2.9 Joint Moments and Cumulants 56 1.2.10 Characteristic Functions 57 1.2.11 Probability Generating Functions 58 1.2.12 Order Statistics 61 1.2.13 Truncation and Censoring 62 1.2.14 Mixture Distributions 64 1.2.15 Variance of a Function 65 1.2.16 Estimation 66 1.2.17 General Comments on the Computer Generation of Discrete Random Variables 71 1.2.18 Computer Software 73 2 Families of Discrete Distributions 74 2.1 Lattice Distributions 74 2.2 Power Series Distributions 75 2.2.1 Generalized Power Series Distributions 75 2.2.2 Modified Power Series Distributions 79 2.3 Difference-Equation Systems 82 2.3.1 Katz and Extended Katz Families 82 2.3.2 Sundt and Jewell Family 85 2.3.3 Ord’s Family 87 2.4 Kemp Families 89 2.4.1 Generalized Hypergeometric Probability Distributions 89 2.4.2 Generalized Hypergeometric Factorial Moment Distributions 96 2.5 Distributions Based on Lagrangian Expansions 99 2.6 Gould and Abel Distributions 101 2.7 Factorial Series Distributions 103 2.8 Distributions of Order-k 105 2.9 q-Series Distributions 106 3 Binomial Distribution 108 3.1 Definition 108 3.2 Historical Remarks and Genesis 109 3.3 Moments 109 3.4 Properties 112 3.5 Order Statistics 116 3.6 Approximations, Bounds, and Transformations 116 3.6.1 Approximations 116 3.6.2 Bounds 122 3.6.3 Transformations 123 3.7 Computation, Tables, and Computer Generation 124 3.7.1 Computation and Tables 124 3.7.2 Computer Generation 125 3.8 Estimation 126 3.8.1 Model Selection 126 3.8.2 Point Estimation 126 3.8.3 Confidence Intervals 130 3.8.4 Model Verification 133 3.9 Characterizations 134 3.10 Applications 135 3.11 Truncated Binomial Distributions 137 3.12 Other Related Distributions 140 3.12.1 Limiting Forms 140 3.12.2 Sums and Differences of Binomial-Type Variables 140 3.12.3 Poissonian Binomial, Lexian, and Coolidge Schemes 144 3.12.4 Weighted Binomial Distributions 149 3.12.5 Chain Binomial Models 151 3.12.6 Correlated Binomial Variables 151 4 Poisson Distribution 156 4.1 Definition 156 4.2 Historical Remarks and Genesis 156 4.2.1 Genesis 156 4.2.2 Poissonian Approximations 160 4.3 Moments 161 4.4 Properties 163 4.5 Approximations, Bounds, and Transformations 167 4.6 Computation, Tables, and Computer Generation 170 4.6.1 Computation and Tables 170 4.6.2 Computer Generation 171 4.7 Estimation 173 4.7.1 Model Selection 173 4.7.2 Point Estimation 174 4.7.3 Confidence Intervals 176 4.7.4 Model Verification 178 4.8 Characterizations 179 4.9 Applications 186 4.10 Truncated and Misrecorded Poisson Distributions 188 4.10.1 Left Truncation 188 4.10.2 Right Truncation and Double Truncation 191 4.10.3 Misrecorded Poisson Distributions 193 4.11 Poisson–Stopped Sum Distributions 195 4.12 Other Related Distributions 196 4.12.1 Normal Distribution 196 4.12.2 Gamma Distribution 196 4.12.3 Sums and Differences of Poisson Variates 197 4.12.4 Hyper-Poisson Distributions 199 4.12.5 Grouped Poisson Distributions 202 4.12.6 Heine and Euler Distributions 205 4.12.7 Intervened Poisson Distributions 205 5 Negative Binomial Distribution 208 5.1 Definition 208 5.2 Geometric Distribution 210 5.3 Historical Remarks and Genesis of Negative Binomial Distribution 212 5.4 Moments 215 5.5 Properties 217 5.6 Approximations and Transformations 218 5.7 Computation and Tables 220 5.8 Estimation 222 5.8.1 Model Selection 222 5.8.2 P Unknown 222 5.8.3 Both Parameters Unknown 223 5.8.4 Data Sets with a Common Parameter 226 5.8.5 Recent Developments 227 5.9 Characterizations 228 5.9.1 Geometric Distribution 228 5.9.2 Negative Binomial Distribution 231 5.10 Applications 232 5.11 Truncated Negative Binomial Distributions 233 5.12 Related Distributions 236 5.12.1 Limiting Forms 236 5.12.2 Extended Negative Binomial Model 237 5.12.3 Lagrangian Generalized Negative Binomial Distribution 239 5.12.4 Weighted Negative Binomial Distributions 240 5.12.5 Convolutions Involving Negative Binomial Variates 241 5.12.6 Pascal–Poisson Distribution 243 5.12.7 Minimum (Riff–Shuffle) and Maximum Negative Binomial Distributions 244 5.12.8 Condensed Negative Binomial Distributions 246 5.12.9 Other Related Distributions 247 6 Hypergeometric Distributions 251 6.1 Definition 251 6.2 Historical Remarks and Genesis 252 6.2.1 Classical Hypergeometric Distribution 252 6.2.2 Beta–Binomial Distribution, Negative (Inverse) Hypergeometric Distribution: Hypergeometric Waiting-Time Distribution 253 6.2.3 Beta–Negative Binomial Distribution: Beta–Pascal Distribution, Generalized Waring Distribution 256 6.2.4 Pólya Distributions 258 6.2.5 Hypergeometric Distributions in General 259 6.3 Moments 262 6.4 Properties 265 6.5 Approximations and Bounds 268 6.6 Tables Computation and Computer Generation 271 6.7 Estimation 272 6.7.1 Classical Hypergeometric Distribution 273 6.7.2 Negative (Inverse) Hypergeometric Distribution: Beta–Binomial Distribution 274 6.7.3 Beta–Pascal Distribution 276 6.8 Characterizations 277 6.9 Applications 279 6.9.1 Classical Hypergeometric Distribution 279 6.9.2 Negative (Inverse) Hypergeometric Distribution: Beta–Binomial Distribution 281 6.9.3 Beta–Negative Binomial Distribution: Beta–Pascal Distribution, Generalized Waring Distribution 283 6.10 Special Cases 283 6.10.1 Discrete Rectangular Distribution 283 6.10.2 Distribution of Leads in Coin Tossing 286 6.10.3 Yule Distribution 287 6.10.4 Waring Distribution 289 6.10.5 Narayana Distribution 291 6.11 Related Distributions 293 6.11.1 Extended Hypergeometric Distributions 293 6.11.2 Generalized Hypergeometric Probability Distributions 296 6.11.3 Generalized Hypergeometric Factorial Moment Distributions 298 6.11.4 Other Related Distributions 299 7 Logarithmic and Lagrangian Distributions 302 7.1 Logarithmic Distribution 302 7.1.1 Definition 302 7.1.2 Historical Remarks and Genesis 303 7.1.3 Moments 305 7.1.4 Properties 307 7.1.5 Approximations and Bounds 309 7.1.6 Computation, Tables, and Computer Generation 310 7.1.7 Estimation 311 7.1.8 Characterizations 315 7.1.9 Applications 316 7.1.10 Truncated and Modified Logarithmic Distributions 317 7.1.11 Generalizations of the Logarithmic Distribution 319 7.1.12 Other Related Distributions 321 7.2 Lagrangian Distributions 325 7.2.1 Otter’s Multiplicative Process 326 7.2.2 Borel Distribution 328 7.2.3 Consul Distribution 329 7.2.4 Geeta Distribution 330 7.2.5 General Lagrangian Distributions of the First Kind 331 7.2.6 Lagrangian Poisson Distribution 336 7.2.7 Lagrangian Negative Binomial Distribution 340 7.2.8 Lagrangian Logarithmic Distribution 341 7.2.9 Lagrangian Distributions of the Second Kind 342 8 Mixture Distributions 343 8.1 Basic Ideas 343 8.1.1 Introduction 343 8.1.2 Finite Mixtures 344 8.1.3 Varying Parameters 345 8.1.4 Bayesian Interpretation 347 8.2 Finite Mixtures of Discrete Distributions 347 8.2.1 Parameters of Finite Mixtures 347 8.2.2 Parameter Estimation 349 8.2.3 Zero-Modified and Hurdle Distributions 351 8.2.4 Examples of Zero-Modified Distributions 353 8.2.5 Finite Poisson Mixtures 357 8.2.6 Finite Binomial Mixtures 358 8.2.7 Other Finite Mixtures of Discrete Distributions 359 8.3 Continuous and Countable Mixtures of Discrete Distributions 360 8.3.1 Properties of General Mixed Distributions 360 8.3.2 Properties of Mixed Poisson Distributions 362 8.3.3 Examples of Poisson Mixtures 365 8.3.4 Mixtures of Binomial Distributions 373 8.3.5 Examples of Binomial Mixtures 374 8.3.6 Other Continuous and Countable Mixtures of Discrete Distributions 376 8.4 Gamma and Beta Mixing Distributions 378 9 Stopped-Sum Distributions 381 9.1 Generalized and Generalizing Distributions 381 9.2 Damage Processes 386 9.3 Poisson–Stopped Sum (Multiple Poisson) Distributions 388 9.4 Hermite Distribution 394 9.5 Poisson–Binomial Distribution 400 9.6 Neyman Type A Distribution 403 9.6.1 Definition 403 9.6.2 Moment Properties 405 9.6.3 Tables and Approximations 406 9.6.4 Estimation 407 9.6.5 Applications 409 9.7 Pólya–Aeppli Distribution 410 9.8 Generalized Pólya–Aeppli (Poisson–Negative Binomial) Distribution 414 9.9 Generalizations of Neyman Type A Distribution 416 9.10 Thomas Distribution 421 9.11 Borel–Tanner Distribution: Lagrangian Poisson Distribution 423 9.12 Other Poisson–Stopped Sum (multiple Poisson) Distributions 425 9.13 Other Families of Stopped-Sum Distributions 426 10 Matching, Occupancy, Runs, and q-Series Distributions 430 10.1 Introduction 430 10.2 Probabilities of Combined Events 431 10.3 Matching Distributions 434 10.4 Occupancy Distributions 439 10.4.1 Classical Occupancy and Coupon Collecting 439 10.4.2 Maxwell–Boltzmann, Bose–Einstein, and Fermi–Dirac Statistics 444 10.4.3 Specified Occupancy and Grassia–Binomial Distributions 446 10.5 Record Value Distributions 448 10.6 Runs Distributions 450 10.6.1 Runs of Like Elements 450 10.6.2 Runs Up and Down 453 10.7 Distributions of Order k 454 10.7.1 Early Work on Success Runs Distributions 454 10.7.2 Geometric Distribution of Order k 456 10.7.3 Negative Binomial Distributions of Order k 458 10.7.4 Poisson and Logarithmic Distributions of Order k 459 10.7.5 Binomial Distributions of Order k 461 10.7.6 Further Distributions of Order k 463 10.8 q-Series Distributions 464 10.8.1 Terminating Distributions 465 10.8.2 q-Series Distributions with Infinite Support 470 10.8.3 Bilateral q-Series Distributions 474 10.8.4 q-Series Related Distributions 476 11 Parametric Regression Models and Miscellanea 478 11.1 Parametric Regression Models 478 11.1.1 Introduction 478 11.1.2 Tweedie–Poisson Family 480 11.1.3 Negative Binomial Regression Models 482 11.1.4 Poisson Lognormal Model 483 11.1.5 Poisson–Inverse Gaussian (Sichel) Model 484 11.1.6 Poisson Polynomial Distribution 487 11.1.7 Weighted Poisson Distributions 488 11.1.8 Double-Poisson and Double-Binomial Distributions 489 11.1.9 Simplex–Binomial Mixture Model 490 11.2 Miscellaneous Discrete Distributions 491 11.2.1 Dandekar’s Modified Binomial and Poisson Models 491 11.2.2 Digamma and Trigamma Distributions 492 11.2.3 Discrete Adès Distribution 494 11.2.4 Discrete Bessel Distribution 495 11.2.5 Discrete Mittag–Leffler Distribution 496 11.2.6 Discrete Student’s t Distribution 498 11.2.7 Feller–Arley and Gegenbauer Distributions 499 11.2.8 Gram–Charlier Type B Distributions 501 11.2.9 “Interrupted” Distributions 502 11.2.10 Lost-Games Distributions 503 11.2.11 Luria–Delbrück Distribution 505 11.2.12 Naor’s Distribution 507 11.2.13 Partial-Sums Distributions 508 11.2.14 Queueing Theory Distributions 512 11.2.15 Reliability and Survival Distributions 514 11.2.16 Skellam–Haldane Gene Frequency Distribution 519 11.2.17 Steyn’s Two-Parameter Power Series Distributions 521 11.2.18 Univariate Multinomial-Type Distributions 522 11.2.19 Urn Models with Stochastic Replacements 524 11.2.20 Zipf-Related Distributions 526 11.2.21 Haight’s Zeta Distributions 533 Bibliography 535 Abbreviations 631 Index 633

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Book SynopsisComplete coverage of the prediction approach to survey sampling in a single resource Prediction theory has been extremely influential in survey sampling for nearly three decades, yet research findings on this model-based approach are scattered in disparate areas of the statistical literature.Trade Review"Valliant...is joined...to dispel the perception of dichotomy between mainstream statistics...and survey sampling..." (SciTech Book News, Vol. 24, No. 4, December 2000) "The vast majority of the book is devoted to prediction of a population mean or total, and as such it forms a cohesive and comprehensive treatment of the subject." (Mathematical Reviews, Issue 2001j) "A highly recommended book which is an essential read for all research workers in this area." (Short Book Reviews - Publication of the Int. Statistical Institute, December 2001) "This book is a welcome addition to the subject of survey sampling." (Zentralblatt MATH, Vol. 964, 2001/14)Table of ContentsIntroduction to Prediction Theory. Prediction Theory Under the General Linear Model. Bias-Robustness. Robustness and Efficiency. Variance Estimation. Stratified Populations. Models with Qualitative Auxiliaries. Clustered Populations. Robust Variance Estimation in Two-Stage Cluster Sampling. Alternative Variance Estimation Methods. Special Topics and Open Questions. Appendices. Bibliography. Answers to Select Exercises. Indexes.

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Book SynopsisWith the growing complexity of engineered systems, reliability has increased in importance throughout the twentieth century. Initially developed to meet practical needs, reliability theory has become an applied mathematical discipline that permits a priori evaluations of various reliability indices at the design stages.Table of ContentsFundamentals. Reliability Indexes. Unrepairable Systems. Load-Strength Reliability Models. Distributions with Monotone Intensity Functions. Repairable Systems. Repairable Duplicated Systems. Analysis of Performance Effectiveness. Two-Pole Networks. Optimal Redundancy. Optimal Technical Diagnosis. Additional Optimization Problems in Reliability Theory. Heuristic Methods in Reliability. Index.

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Book SynopsisRegression analysis is the study of the dependence of a response variable on one or more predictor variables. It is among the most widely used methods in statistics. In recent years, several new ways to approach regression have been presented.Trade Review"...with its up-to-date discussion of regression graphics at a very accessible level, Applied Regression Including Computing and Graphics is a must for everyone working in the area of regression analysis. I strongly recommend it as a text..." (Journal of the American Statistical Association, September 2001) "...a must for everyone working in the area of regression analysis. I strongly recommend it as a text..." (Journal of the American Statistical Association, September 2001)Table of ContentsLooking Forward and Back. Introduction to Regression. Introduction to Smoothing. Bivariate Distributions. Two-Dimensional Plots. TOOLS. Simple Linear Regression. Introduction to Multiple Linear Regression. Three-Dimensional Plots. Weights and Lack-of-Fit. Understanding Coefficients. Relating Mean Functions. Factors and Interactions. Response Transformations. Diagnostics I: Curvature and Nonconstant Variance. Diagnostics II: Influence and Outliers. Predictor Transformations. Model Assessment. REGRESSION GRAPHICS. Visualizing Regression. Visualizing Regression with Many Predictors. Graphical Regression. LOGISTIC REGRESSION AND GENERALIZED LINEAR MODELS. Binomial Regression. Graphical and Diagnostic Methods for Logistic Regression. Generalized Linear Models. Appendix. References. Indexes.

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Book SynopsisThis text combines lucid and statistically engaging exposition, graphic and applied examples, and realistic exercise settings, to take students past the mechanics of introductory-level statistical techniques into the realm of practical data analysis, and inference-based problem solving.Trade Review"...a superb book....Wild & Seber have now raised the standard of introductory textbooks another notch." (Australian & New Zealand, 2000)Table of ContentsWhat Is Statistics? Tools for Exploring Univariate Data. Exploratory Tools for Relationships. Probabilities and Proportions. Discrete Random Variables. Continuous Random Variables. Sampling Distributions of Estimates. Confidence Intervals. Significance Testing: Using Data to Test Hypotheses. Data on a Continuous Variable. Tables of Counts. Relationships between Quantitative Variables: Regression and Correlation. Control Charts. Time Series. Appendices. References. Answers to Selected Problems. Index.

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Book SynopsisThis revised and updated volume describes methods fundamental to the theory and explanation of data analysis. This edition includes extensions and devices such as component and component-plus residual plots, cross-verification with a second sample and an index of required x-precision.Trade Review"...a grand historical document for industrial statistics in its glory days, as its selection for the Classics Library implies." --Technometrics Vol. 42, No. 4 May 2001 This book provides an excellent insight into the minds of two master craftsmen at work. I very much applaud the decision to include this in a "classics library" and would encourage more authors to produce statistics books in the same vein, i.e. focused on the practical application of the subject rather than methodology development. Anyone involved in the analysis of unbalanced multifactor dtaa will find this book an extremely useful source of practical advice. --The Statistician 50 (1) 2001.Table of ContentsAssumptions and Methods of Fitting Equations. One Independent Variable. Two or More Independent Variables. Fitting an Equation in Three Independent Variables. Selection of Independent Variables. Some Consequences of the Disposition of the Data Points. Selection of Variables in Nested Data. Nonlinear Least Squares, a Complex Example. Glossary. User's Manual. Bibliography. Index.

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Book SynopsisThis book illustrates scientific methodology through descriptions of how actual scientists create science. The authors present a novel point of view, arguing that the popular perception of science as being strictly objective is untrue and that knowledge is often acquired through very personal means.Trade Review"Press and Tanur argue that subjectivity has not only played a significant role in the advancement of science, but that science will advance more rapidly if the modern methods of Bayesian statistical analysis replace some of the more classical twentieth-century methods." (SciTech Book News, Vol. 25, No. 3, September 2001) "An insightful work." (Choice, Vol. 39, No. 4, December 2001) "compilation of interesting and popular problems" (Short Book Reviews - Publication of the Int. Statistical Institute, December 2001) "...this book is fascinating." (Short Book Reviews, Vol. 21, No. 3, December 2001) "...highlight the role of subjectivity in science by describing the life and works of 17 scientists." (Zentralblatt MATH, Vol. 973, 2001/23)Table of ContentsPrefaceix 1. Introduction 1 2. Selecting the Scientists 17 3. Some Well Known Stories of Extreme Subjectivity 23 3.1 Introduction 23 3.2 Johannes Kepler 23 3.3 Gregor Mendel 26 3.4 Robert Millikan 34 3.5 Cyril Burt 37 3.6 Margaret Mead 43 4. Stories of Famous Scientists 49 4.1 Introduction 49 4.2 Aristotle 51 4.3 Galileo Galilei 60 4.4 William Harvey 71 4.5 Sir Isaac Newton 81 4.6 Antoine Lavoisier 95 4.7 Alexander von Humboldt 110 4.8 Michael Faraday 121 4.9 Charles Darwin 128 4.10 Louis Pasleur 143 4.11 Sigmund Freud 156 4.12 Marie Curie 166 4.13 Albert Einstein 177 4.14 Same Conjecrures About the Scientists 189 5. Subjectivity .in Science in Modern Times: The Bayesian Approach199 Appendix: References by Field of Application for Bayesian Statistical Science225 Bibliography 231 Subject Index 249 Name Index 267

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Book SynopsisThe information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear Model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression of ANOVA than do traditional sums of squares and scalar equations. The book contains a balanced treatment of regression and ANOVA yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text contains almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided, and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only real data, collected in the course of scientific research. The book is divided Trade Review“…very useful to applied scientists and for graduate level courses in areas of non-mathematical statistics…” (Zentralblatt Math, Vol.1039, No.8, 2004)Table of ContentsPreface. Examples and Limits of the GLM. Statement of the Model, Estimation, and Testing. Some Distributions for the GLM. Multiple Regression: General Considerations. Testing Hypotheses in Multiple Regression. Correlations. GLM Assumption Diagnostics. GLM Computation Diagnostics. Polynomial Regression. Transformations. Selecting the Best Model. Coding Schemes for Regression. One-Way ANOVA. Complete, Two-Way Factorial ANOVA. Special Cases of Two-Way ANOVA and Random Effects Basics. The Full Model in Every Cell (ANCOVA as a Special Case). Understanding and Computing Power for the GLM. Appendix A. Matrix Algebra for Linear Models. Appendix B. Statistical Tables. Appendix C. Study Guide for Linear Model Theory. Appendix D. Homework and Example Data. Appendix E. Introduction to SAS/IML. Appendix F. A Brief Manual to LINMOD. Appendix G. SAS/IML Power Program User's Guide. Appendix H. Regression Model Selection Data. References. Index.

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Book SynopsisFast technological advances have allowed investors and traders to make increasingly sophisticated analysis of market momentum. The current trend in the financial world continues towards momentum analysis. This book looks at both the theory and the application.Table of ContentsTHEORY. Introduction. Momentum Preliminaries. Indicator Creation. Indicator Selection. Indicator Combination. System Maintenance. Risk Management. Summary. APPLICATION. Spreadsheet Preliminaries. How to Apply Indicator Creation. How to Apply Indicator Selection. How to Apply Indicator Combination. Performance and Maintenance. Appendix 1: Excel Functions. Appendix 2: Indicator Variations. Glossary. Index.

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Book SynopsisIn modern society, we are ever more aware of the environmentalissues we face, whether these relate to global warming, depletionof rivers and oceans, despoliation of forests, pollution of land,poor air quality, environmental health issues, etc. At the mostfundamental level it is necessary to monitor what is happening inthe environment - collecting data to describe the changingscene. More importantly, it is crucial to formally describe theenvironment with sound and validated models, and to analyse andinterpret the data we obtain in order to take action. Environmental Statistics provides a broad overview of thestatistical methodology used in the study of the environment,written in an accessible style by a leading authority on thesubject. It serves as both a textbook for students of environmentalstatistics, as well as a comprehensive source of reference foranyone working in statistical investigation of environmentalissues. * Provides broad coverage of the methodology used in tTrade Review"Inspired by the Encyclopedia of Statistical Sciences, SecondEdition (ESS2e), this volume presents a concise, well-rounded focuson the statistical concepts and applications that are essential forunderstanding gathered data in the fields of engineering, qualitycontrol, and the physical sciences. The book successfully upholdsthe goals of ESS2e by combining both previously-published and newlydeveloped contributions written by over 100 leading academics,researchers, and practitioner in a comprehensive, approachableformat. The result is a succinct reference that unveils modern,cutting-edge approaches to acquiring and analyzing data acrossdiverse subject areas within these three disciplines, includingoperations research, chemistry, physics, the earth sciences,electrical engineering, and quality assurance." (Finwin, 7September 2011) "In this book, Vic Barnett, a distinguished environmentalstatistician, provides an overview of statistical methods that havebeen used on such problems in the environmental sciences."(Journal of the American Statistical Association, September2006) "...combines sound fundamentals and their applications."(European Journal of Soil Science, No.56, April 2005) "Many tables, graphs and figures illustrate the environmentalapplications of the statistical methods that are described."(Journal of the Royal Statistical Society, Series A,Vol.168, No.2, March 2005) "...well written...methods are illustrated with interestingexamples...a comprehensive reference source for anyone working onenvironmental issues..." (Short Book Reviews, Vol.24, No.3,December 2004) "Statisticians should enjoy the book. The author is an extremelyknowledgeable statistician, and he is writing about an applicationdomain that he clearly knows." (Technometrics, November2004) "An excellent book. Highly recommended." (Choice, July2004) "...this provides an excellent sketch of the current state ofdevelopment for new statistical methodologies...a valuableresource..." (Statistics in Medicine, 15th August 2005)Table of ContentsPreface. Chapter 1: Introduction. 1.1 Tomorrow is too Late! 1.2 Environmental Statistics. 1.3 Some Examples. 1.3.1 ‘Getting it all together’. 1.3.2 ‘In time and space’. 1.3.3 ‘Keep it simple’. 1.3.4 ‘How much can we take?’ 1.3.5 ‘Over the top’. 1.4 Fundamentals. 1.5 Bibliography. PART I: EXTREMAL STRESSES: EXTREMES, OUTLIERS, ROBUSTNESS. Chapter 2: Ordering and Extremes: Applications, models, inference. 2.1 Ordering the Sample. 2.1.1 Order statistics. 2.2 Order-based Inference. 2.3 Extremes and Extremal Processes. 2.3.1 Practical study and empirical models; generalized extreme-value distributions. 2.4 Peaks over Thresholds and the Generalized Pareto Distribution. Chapter 3: Outliers and Robustness. 3.1 What is an Outlier? 3.2 Outlier Aims and Objectives. 3.3 Outlier-Generating Models. 3.3.1 Discordancy and models for outlier generation. 3.3.2 Tests of discordancy for specific distributions. 3.4 Multiple Outliers: Masking and Swamping. 3.5 Accommodation: Outlier-Robust Methods. 3.6 A Possible New Approach to Outliers. 3.7 Multivariate Outliers. 3.8 Detecting Multivariate Outliers. 3.8.1 Principles. 3.8.2 Informal methods. 3.9 Tests of Discordancy. 3.10 Accommodation. 3.11 Outliers in linear models. 3.12 Robustness in General. PART II: COLLECTING ENVIRONMENTAL DATA: SAMPLING AND MONITORING. Chapter 4: Finite-Population Sampling. 4.1 A Probabilistic Sampling Scheme. 4.2 Simple Random Sampling. 4.2.1 Estimating the mean, &Xmacr;. 4.2.2 Estimating the variance, S2. 4.2.3 Choice of sample size, n. 4.2.4 Estimating the population total, XT. 4.2.5 Estimating a proportion, P. 4.3 Ratios and Ratio Estimators. 4.3.1 The estimation of a ratio. 4.3.2 Ratio estimator of a population total or mean. 4.4 Stratified (simple) Random Sampling. 4.4.1 Comparing the simple random sample mean and the stratified sample mean. 4.4.2 Choice of sample sizes. 4.4.3 Comparison of proportional allocation and optimum allocation. 4.4.4 Optimum allocation for estimating proportions. 4.5 Developments of Survey Sampling. Chapter 5: Inaccessible and Sensitive Data. 5.1 Encountered Data. 5.2 Length-Biased or Size-Biased Sampling and Weighted Distributions. 5.2.1 Weighted distribution methods. 5.3 Composite Sampling. 5.3.1 Attribute Sampling. 5.3.2 Continuous variables. 5.3.3 Estimating mean and variance. 5.4 Ranked-Set Sampling. 5.4.1 The ranked-set sample mean. 5.4.2 Optimal estimation. 5.4.3 Ranked-set sampling for normal and exponential distributions. 5.4.4 Imperfect ordering. Chapter 6: Sampling in the Wild. 6.1 Quadrat Sampling. 6.2 Recapture Sampling. 6.2.1 The Petersen and Chapman estimators. 6.2.2 Capture–recapture methods in open populations. 6.3 Transect Sampling. 6.3.1 The simplest case: strip transects. 6.3.2 Using a detectability function. 6.3.3 Estimating f (y). 6.3.4 Modifications of approach. 6.3.5 Point transects or variable circular plots. 6.4 Adaptive Sampling. 6.4.1 Simple models for adaptive sampling. Part III: EXAMINING ENVIRONMENTAL EFFECTS: STIMULUS–RESPONSE RELATIONSHIPS. Chapter 7: Relationship: regression-type models and methods. 7.1 Linear Models. 7.1.1 The linear model. 7.1.2 The extended linear model. 7.1.3 The normal linear model. 7.2 Transformations. 7.2.1 Looking at the data. 7.2.2 Simple transformations. 7.2.3 General transformations. 7.3 The Generalized Linear Model. Chapter 8: Special Relationship Models, Including Quantal Response and Repeated Measures. 8.1 Toxicology Concerns. 8.2 Quantal Response. 8.3 Bioassay. 8.4 Repeated Measures. Part IV: STANDARDS AND REGULATIONS. Chapter 9: Environmental Standards. 9.1 Introduction. 9.2 The Statistically Verifiable Ideal Standard. 9.2.1 Other sampling methods. 9.3 Guard Point Standards. 9.4 Standards Along the Cause–Effect Chain. Part V: A MANY-DIMENSIONAL ENVIRONMENT: SPATIAL AND TEMPORAL PROCESSES. Chapter 10: Time-Series Methods. 10.1 Space and Time Effects. 10.2 Time Series. 10.3 Basic Issues. 10.4 Descriptive Methods. 10.4.1 Estimating or eliminating trend. 10.4.2 Periodicities. 10.4.3 Stationary time series. 10.5 Time-Domain Models and Methods. 10.6 Frequency-Domain Models and Methods. 10.6.1 Properties of the spectral representation. 10.6.2 Outliers in time series. 10.7 Point Processes. 10.7.1 The Poisson process. 10.7.2 Other point processes. Chapter 11: Spatial Methods for Environmental Processes. 11.1 Spatial Point Process Models and Methods. 11.2 The General Spatial Process. 11.2.1 Predication, interpolation and kriging. 11.2.2 Estimation of the variogram. 11.2.3 Other forms of kriging. 11.3 More about Standards Over Space and Time. 11.4 Relationship. 11.5 More about Spatial Models. 11.5.1 Types of spatial model. 11.5.2 Harmonic analysis of spatial processes. 11.6 Spatial Sampling and Spatial Design. 11.6.1 Spatial sampling. 11.6.2 Spatial design. 11.7 Spatial-Temporal Models and Methods. References. Index.

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Book SynopsisNonlinear Bayesian modelling is a relatively new field, but one that has seen a recent explosion of interest. Nonlinear models offer more flexibility than those with linear assumptions, and their implementation has now become much easier due to increases in computational power. Bayesian methods allow for the incorporation of prior information, allowing the user to make coherent inference. Bayesian Methods for Nonlinear Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian methods. * Focuses on the problems of classification and regression using flexible, data-driven approaches. * Demonstrates how Bayesian ideas can be used to improve existing statistical methods. * Includes coverage of Bayesian additive models, decision trees, nearest-neighbour, wavelets, regression splines, and neural networks. * Emphasis is placed on sound implementation of nonlinear models. * DiscussTrade Review"The exercises and the excellent presentation style make this book qualified t be a textbook in a graduate level nonlinear regression course." (Journal of Statistical Computation and Simulation, July 2005) "Its in-depth coverage of implementation issues and detailed discussion of pros and cons of different modeling strategies make it attractive for many researchers.” (Technometrics, May 2004) "...a fascinating account of a rapidly evolving area of statistics..." (Short Book Reviews, December 2002) "...will benefit researchers...also suitable for graduate students..." (Mathematical Reviews, 2003m)Table of ContentsPreface Acknowledgements. Introduction Bayesian Modelling Curve Fitting Surface Fitting Classification using Generalised Nonlinear Models Bayesian Tree Models Partition Models Nearest-Neighbour Models Multiple Response Models Appendix A: Probability Distributions Appendix B: Inferential Processes References Index Author Index

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Book SynopsisWith meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field. Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments.Trade Review“Both books can be recommended for graduate training and are useful additions to the library of those interested in the meta-analytic accumulation of literatures on training, vocational learning, and education in the professions.” (Vocations and Learning, 15 December 2010) "This well-written book offers an exhaustive criticism and up-to-date references, illustrates effectively with real life examples and data…" (Journal of Statistical Computation & Simulation, July 2004) "this is an excellent book..." (Short Book Reviews, April 2001) "...recommended for mathematically skilled readers interested in getting an overview of the various methods and the existing literature..." (Statistics in Medicine, 15 October 2003) Table of ContentsPART A: META-ANALYSIS METHODOLOGY: THE BASICS Introduction: Meta-analysis: Its Development and Uses Defining Outcome Measures used for Combining via Meta-analysis Random Effects Models for Combining Study Estimates Exploring Between Study Heterogeneity Publication Bias Study Quality Sensitivity Analysis Reporting the Results of a Meta-analysis Fixed Effects Methods for Combining Study Estimates PART B: ADVANCED AND SPECIALIZED META-ANALYSIS TOPICS Bayesian Methods in Meta-analysis Meta Regression Meta-analysis of Different Types of Data Incorporating Study Quality into a Meta-analysis Meta-analysis of Multiple and Correlated Outcome Measures Meta-analysis of Epidemiological and other Observational Studies Generalised Synthesis of Evidence - Combining Different Sources of Evidence Meta-analysis of Survival Data Cumulative Meta-analysis Miscellaneous and Developing Areas of Applications in Meta-Analysis Appendix I: Software Used for the Examples in this Book

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Book SynopsisQueueing Systems Volume 1: Theory Leonard Kleinrock This book presents and develops methods from queueing theory in sufficient depth so that students and professionals may apply these methods to many modern engineering problems, as well as conduct creative research in the field.Table of ContentsA Queueing Theory Primer. Bounds. Inequalities and Approximations. Priority Queueing. Computer Time-Sharing and Multiaccess Systems. Computer-Communication Networks: Analysis and Design. Computer-Communication Networks: Measurement, Flow Control, and ARPANET Traps.

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