Computational Statistics Handbook with MATLAB (Chapman & Hall/CRC Computer Science & Data Analysis)

Approaching computational statistics through its theoretical aspects can be daunting. Often intimidated or distracted by the theory, researchers and students can lose sight of the actual goals and applications of the subject. What they need are its key concepts, an understanding of its methods, experience with its implementation, and practice with computational software. Focusing on the computational aspects of statistics rather than the theoretical, Computational Statistics Handbook with MATLAB uses a down-to-earth approach that makes statistics accessible to a wide range of users. The authors integrate the use of MATLAB throughout the book, allowing readers to see the actual implementation of algorithms, but also include step-by-step procedures to allow implementation with any suitable software. The book concentrates on the simulation/Monte Carlo point of view, and contains algorithms for exploratory data analysis, modeling, Monte Carlo simulation, pattern recognition, bootstrap, classification, cross-validation methods, probability density estimation, random number generation, and other computational statistics methods. Emphasis on the practical aspects of statistics, details of the latest techniques, and real implementation experience make the Computational Statistics Handbook with MATLAB more than just the first book to use MATLAB to solve computational problems in statistics. It also forms an outstanding, introduction to statistics for anyone in the many disciplines that involve data analysis. COVER 1 fm 2 Computational Statistics Handbook with MATLAB® 2 Table of Contents 5 Preface 13 Chapter 1 16 Computational Statistics Handbook with MATLAB® 16 Chapter 1: Introduction 16 1.1 What Is Computational Statistics? 16 1.2 An Overview of the Book 17 Philosphy 17 Wh What Is t Covere Covered 18 A Word About N Notation ion 20 1.3 MATLAB Code 21 Computational Statist Statistics Toolbox oolbox 22 Internet Resourc Resources 22 1.4 Further Reading 23 Chapter 2 25 Computational Statistics Handbook with MATLAB® 25 Chapter 2: Probability Concepts 25 2.1 Introduction 25 2.2 Probability 26 Background 26 Probability 28 Axioms of Probability 31 2.3 Conditional Probability and Independence 31 Conditional Probability 31 Independence 32 Bayes Theorem 33 2.4 Expectation 35 Mean and Variance 35 Skewness 37 Kurtosis 37 2.5 Common Distributions 38 Binomial 38 Example 2.1 40 Example 2.2 42 Example 2.3 42 Example 2.4 43 Example 2.5 46 Example 2.6 49 Example 2.7 51 Example 2.8 53 Example 2.9 55 Example 2.10 57 2.6 MATLAB Code 59 2.7 Further Reading 60 Exercises 62 Chapter 3 65 Computational Statistics Handbook with MATLAB® 65 Chapter 3: Sampling Concepts 65 3.1 Introduction 65 3.2 Sampling Terminology and Concepts 65 Sample Mean and Sample Variance 67 Sample Moments 68 Example 3.1 69 Covariance 70 Example 3.2 71 3.3 Sampling Distributions 72 3.4 Parameter Estimation 74 Bias 75 Mean Squared Error 75 Relative Efficiency 76 Standard Error 76 Maximum Likelihood Estimation 77 Example 3.3 78 Method of Moments 80 Example 3.4 80 3.5 Empirical Distribution Function 82 Quantiles 83 Example 3.5 85 Example 3.6 86 3.6 MATLAB Code 87 3.7 Further Reading 88 Exercises 90 Chapter 4 92 Computational Statistics Handbook with MATLAB® 92 Chapter 4: Generating Random Variables 92 4.1 Introduction 92 4.2 General Techniques for Generating Random Variables 92 Uniform Random Numbers 92 Example 4.1 93 Inverse Transform Method 95 Example 4.2 96 Example 4.3 97 Acceptance-Rejection Method 98 Example 4.4 99 Example 4.5 101 4.3 Generating Continuous Random Variables 102 Normal Distribution 102 Exponential Distribution 102 Example 4.6 103 Gamma 104 Example 4.7 105 Chi-Square 106 Example 4.8 107 Beta 108 Example 4.9 108 Multivariate Nomal 109 Example 4.10 111 Generating Variates on a Sphere 112 Example 4.11 113 4.4 Generating Discrete Random Variables 113 Binomial 113 Example 4.12 114 Poisson 115 Example 4.13 116 Discrete Uniform 117 Example 4.14 118 4.5 MATLAB Code 119 4.6 Further Reading 120 Exercises 122 Chapter 5 124 Computational Statistics Handbook with MATLAB® 124 Chapter 5: Exploratory Data Analysis 124 5.1 Introduction 124 5.2 Exploring Univariate Data 125 Histograms 126 Example 5.1 126 Example 5.2 128 Stem-and-Leaf 129 Example 5.3 130 Quantile-Based Plots - Continuous Distributions 132 Q-QPlot 132 Example 5.4 133 Example 5.5 134 Quantile Plots 136 Example 5.6 137 Quantile Plots - Discrete Distributions 139 Poissonness Plot 139 Example 5.7 139 Example 5.8 142 Binomialness Plot 142 Example 5.9 143 Box Plots 145 Example 5.10 146 5.3 Exploring Bivariate and Trivariate Data 148 Scatterplots 148 Example 5.11 149 Surface Plots 151 Example 5.12 151 Contour Plots 151 Example 5.13 152 Bivariate Histogram 154 Example 5.14 155 Example 5.15 156 3-D Scatterplot 158 Example 5.16 159 5.4 Exploring Multi- Dimensional Data 160 Scatterplot Matrix 160 Example 5.17 161 Slices and Isosurfaces 162 Example 5.18 162 Example 5.19 164 Example 5.20 166 Star Plots 168 Example 5.21 168 Andrews Curves 170 Example 5.22 171 Example 5.23 173 Parallel Coordinates 175 Example 5.24 175 Example 5.25 176 Example 5.26 177 Projection Pursuit 181 Projection Pursuit Index 184 Finding the Structure 185 Structure Removal 187 Example 5.27 189 Grand Tour 191 Example 5.28 195 5.5 MATLAB Code 196 5.6 Further Reading 197 Exercises 200 Chapter 6 204 Computational Statistics Handbook with MATLAB® 204 Chapter 6: Monte Carlo Methods for Inferential Statistics 204 6.1 Introduction 204 6.2 Classical Inferential Statistics 205 Hypothesis Testing 205 Example 6.1 207 Example 6.2 209 Example 6.3 211 Example 6.4 213 Confidence Intervals 214 Example 6.5 216 6.3 Monte Carlo Methods for Inferential Statistics 217 Basic Monte Carlo Procedure 217 Monte Carlo Hypothesis Testing 218 Example 6.6 219 Example 6.7 222 Monte Carlo Assessment of Hypothesis Testing 223 Example 6.8 224 6.4 Bootstrap Methods 227 General Bootstrap Methodology 227 Bootstrap Estimate of Standard Error 229 Example 6.9 230 Bootstrap Estimate of Bias 232 Example 6.10 233 Bootstrap Confidence Intervals 233 Bootstrap Standard Confidence Interval 233 Bootstrap-t Confidence Interval 234 Example 6.11 235 Bootstrap Percentile Interval 237 Example 6.12 238 6.5 MATLAB Code 239 6.6 Further Reading 240 Exercises 241 Chapter 7 243 Computational Statistics Handbook with MATLAB® 243 Chapter 7: Data Partitioning 243 7.1 Introduction 243 7.2 Cross- Validation 244 Example 7.1 246 Example 7.2 248 Example 7.3 250 7.3 Jackknife 251 Example 7.4 253 Example 7.5 254 Example 7.6 257 Example 7.7 258 7.4 Better Bootstrap Confidence Intervals 259 Example 7.8 262 7.5 Jackknife- After- Bootstrap 263 Example 7.9 264 7.6 MATLAB Code 265 7.7 Further Reading 266 Exercises 268 Chapter 8 270 Computational Statistics Handbook with MATLAB® 270 Chapter 8: Probability Density Estimation 270 8.1 Introduction 270 8.2 Histograms 272 1-D Histograms 272 Example 8.1 274 Example 8.2 277 Multivariate Histograms 278 Frequency Polygons 280 Example 8.3 282 Example 8.4 284 Averaged Shifted Histograms 285 Example 8.5 289 8.3 Kernel Density Estimation 291 Univariate Kernel Estimators 291 Example 8.6 293 Multivariate Kernel Estimators 296 Example 8.7 297 8.4 Finite Mixtures 298 Univariate Finite Mixtures 300 Example 8.8 300 Visualizing Finite Mixtures 302 Example 8.9 302 Multivariate Finite Mixtures 304 Example 8.10 305 EM Algorithm for Estimating the Parameters 307 Example 8.11 310 Adaptive Mixtures 312 Example 8.12 315 8.5 Generating Random Variables 317 Example 8.13 320 8.6 MATLAB Code 322 8.7 Further Reading 322 Exercises 325 Chapter 9 328 Computational Statistics Handbook with MATLAB® 328 Chapter 9: Statistical Pattern Recognition 328 9.1 Introduction 328 9.2 Bayes Decision Theory 330 Estimating Class-Conditional Probabilities: Parametric Method 332 Example 9.1 332 Estimating Class-Conditional Probabilities: Nonparametric 333 Example 9.2 333 Bayes Decision Rule 334 Example 9.3 335 Example 9.4 338 Likelihood Ratio Approach 340 Example 9.5 342 9.3 Evaluating the Classifier 343 Independent Test Sample 344 Example 9.6 344 Cross-Validation 346 Example 9.7 346 Receiver Operating Characteristic (ROC) Curve 348 Example 9.8 351 9.4 Classification Trees 353 Example 9.9 356 Growing the Tree 358 Example 9.10 358 Example 9.11 361 Pruning the Tree 363 Example 9.12 366 Choosing the Best Tree 367 Selecting the Best Tree Using an Independent Test Sample 368 Example 9.13 371 Selecting the Best Tree Using Cross-Validation 372 Example 9.14 375 9.5 Clustering 378 Measures of Distance 378 Example 9.15 379 Hierarchical Clustering 380 Example 9.16 382 Example 9.17 384 K-Means Clustering 384 Example 9.18 386 9.6 MATLAB Code 387 9.7 Further Reading 390 Exercises 392 Chapter 10 395 Computational Statistics Handbook with MATLAB® 395 Chapter 10: Nonparametric Regression 395 10.1 Introduction 395 Example 10.1 396 Example 10.2 398 10.2 Smoothing 400 Loess 401 Example 10.3 403 Robust Loess Smoothing 406 Example 10.4 409 Upper and Lower Smooths 410 Example 10.5 411 10.3 Kernel Methods 411 Nadaraya-Watson Estimator 414 Example 10.6 415 Local Linear Kernel Estimator 415 Example 10.7 417 10.4 Regression Trees 417 Growing a Regression Tree 420 Example 10.8 420 Pruning a Regression Tree 421 Selecting a Tree 422 Example 10.9 427 10.5 MATLAB Code 429 10.6 Further Reading 430 Exercises 432 Chapter 11 434 Computational Statistics Handbook with MATLAB® 434 Chapter 11: Markov Chain Monte Carlo Methods 434 11.1 Introduction 434 11.2 Background 435 Bayesian Inference 435 Monte Carlo Integration 436 Example 11.1 437 Markov Chains 438 Analyzing the Output 439 11.3 Metropolis- Hastings Algorithms 439 Metropolis-Hastings Sampler 440 Example 11.2 441 Metropolis Sampler 442 Example 11.3 445 Independence Sampler 447 Autoregressive Generating Density 448 Example 11.4 448 Example 11.5 450 11.4 The Gibbs Sampler 452 Example 11.6 454 Example 11.7 456 Example 11.8 459 11.5 Convergence Monitoring 461 Gelman and Rubin Method 462 Example 11.9 465 Raftery and Lewis Method 467 11.6 MATLAB Code 467 11.7 Further Reading 470 Exercises 471 Chapter 12 474 Computational Statistics Handbook with MATLAB® 474 Chapter 12: Spatial Statistics 474 12.1 Introduction 474 What Is Spatial Statistics? 474 Types of Spatial Data 475 Spatial Point Patterns 476 Complete Spatial Randomness 478 12.2 Visualizing Spatial Point Processes 480 Example 12.1 480 Example 12.2 481 Example 12.3 482 12.3 Exploring First- order and Second- order Properties 484 Estimating the Intensity 484 Example 12.4 485 Estimating the Spatial Dependence 487 Nearest Neighbor Distances - G and F Distributions 487 K-Function 491 Example 12.5 489 Example 12.6 493 12.4 Modeling Spatial Point Processes 494 Nearest Neighbor Distances 495 Example 12.7 498 K-Function 499 Example 12.8 501 Example 12.9 502 12.5 Simulating Spatial Point Processes 504 Homogeneous Poisson Process 504 Example 12.10 505 Binomial Process 506 Example 12.11 507 Poisson Cluster Process 508 Example 12.12 509 Inhibition Process 510 Example 12.13 511 Strauss Process 513 Example 12.14 513 12.6 MATLAB Code 514 12.7 Further Reading 516 Exercises 517 Appendix A 519 Computational Statistics Handbook with MATLAB® 519 Appendix A: Introduction to MATLAB 519 A. 1 What Is MATLAB? 519 A. 2 Getting Help in MATLAB 520 A. 3 File and Workspace Management 520 A. 4 Punctuation in MATLAB 522 A. 5 Arithmetic Operators 522 A. 6 Data Constructs in MATLAB 524 A. 7 Script Files and Functions 526 A. 8 Control Flow 528 A. 9 Simple Plotting 529 A. 10 Contact Information 532 Appendix B 533 Computational Statistics Handbook with MATLAB® 533 Appendix B: Index of Notation 533 Single Letters 533 Other 533 Greek Symbols 534 Acronyms 535 Appendix C 536 Computational Statistics Handbook with MATLAB® 536 Appendix C: Projection Pursuit Indexes 536 C. 1 Indexes 536 Friedman-Tukey Index 536 Entropy Index 537 Moment Index 537 L2 Distances 538 C. 2 MATLAB Source Code 539 Appendix D 545 Computational Statistics Handbook with MATLAB® 545 Appendix D: MATLAB Code 545 D.1 Bootstrap Confidence Interval BCa 545 D. 2 Adaptive Mixtures Density Estimation 546 D. 3 Classification Trees 548 D. 4 Regression Trees 550 Appendix E 552 Computational Statistics Handbook with MATLAB® 552 Appendix E: MATLAB Statistics Toolbox 552 Appendix F 561 Computational Statistics Handbook with MATLAB® 561 Appendix F 561 Computational Statistics Toolbox 561 Appendix G 566 Computational Statistics Handbook with MATLAB® 566 Appendix G: Data Sets 566 References 573 COVER......Page 1 Computational Statistics Handbook with MATLAB®......Page 2 Table of Contents......Page 5 Preface......Page 13 1.1 What Is Computational Statistics?......Page 16 Philosphy......Page 17 Wh What Is t Covere Covered......Page 18 A Word About N Notation ion......Page 20 1.3 MATLAB Code......Page 21 Internet Resourc Resources......Page 22 1.4 Further Reading......Page 23 2.1 Introduction......Page 25 Background......Page 26 Probability......Page 28 Conditional Probability......Page 31 Independence......Page 32 Bayes Theorem......Page 33 Mean and Variance......Page 35 Kurtosis......Page 37 Binomial......Page 38 Example 2.1......Page 40 Example 2.3......Page 42 Example 2.4......Page 43 Example 2.5......Page 46 Example 2.6......Page 49 Example 2.7......Page 51 Example 2.8......Page 53 Example 2.9......Page 55 Example 2.10......Page 57 2.6 MATLAB Code......Page 59 2.7 Further Reading......Page 60 Exercises......Page 62 3.2 Sampling Terminology and Concepts......Page 65 Sample Mean and Sample Variance......Page 67 Sample Moments......Page 68 Example 3.1......Page 69 Covariance......Page 70 Example 3.2......Page 71 3.3 Sampling Distributions......Page 72 3.4 Parameter Estimation......Page 74 Mean Squared Error......Page 75 Standard Error......Page 76 Maximum Likelihood Estimation......Page 77 Example 3.3......Page 78 Example 3.4......Page 80 3.5 Empirical Distribution Function......Page 82 Quantiles......Page 83 Example 3.5......Page 85 Example 3.6......Page 86 3.6 MATLAB Code......Page 87 3.7 Further Reading......Page 88 Exercises......Page 90 Uniform Random Numbers......Page 92 Example 4.1......Page 93 Inverse Transform Method......Page 95 Example 4.2......Page 96 Example 4.3......Page 97 Acceptance-Rejection Method......Page 98 Example 4.4......Page 99 Example 4.5......Page 101 Exponential Distribution......Page 102 Example 4.6......Page 103 Gamma......Page 104 Example 4.7......Page 105 Chi-Square......Page 106 Example 4.8......Page 107 Example 4.9......Page 108 Multivariate Nomal......Page 109 Example 4.10......Page 111 Generating Variates on a Sphere......Page 112 Binomial......Page 113 Example 4.12......Page 114 Poisson......Page 115 Example 4.13......Page 116 Discrete Uniform......Page 117 Example 4.14......Page 118 4.5 MATLAB Code......Page 119 4.6 Further Reading......Page 120 Exercises......Page 122 5.1 Introduction......Page 124 5.2 Exploring Univariate Data......Page 125 Example 5.1......Page 126 Example 5.2......Page 128 Stem-and-Leaf......Page 129 Example 5.3......Page 130 Q-QPlot......Page 132 Example 5.4......Page 133 Example 5.5......Page 134 Quantile Plots......Page 136 Example 5.6......Page 137 Example 5.7......Page 139 Binomialness Plot......Page 142 Example 5.9......Page 143 Box Plots......Page 145 Example 5.10......Page 146 Scatterplots......Page 148 Example 5.11......Page 149 Contour Plots......Page 151 Example 5.13......Page 152 Bivariate Histogram......Page 154 Example 5.14......Page 155 Example 5.15......Page 156 3-D Scatterplot......Page 158 Example 5.16......Page 159 Scatterplot Matrix......Page 160 Example 5.17......Page 161 Example 5.18......Page 162 Example 5.19......Page 164 Example 5.20......Page 166 Example 5.21......Page 168 Andrews Curves......Page 170 Example 5.22......Page 171 Example 5.23......Page 173 Example 5.24......Page 175 Example 5.25......Page 176 Example 5.26......Page 177 Projection Pursuit......Page 181 Projection Pursuit Index......Page 184 Finding the Structure......Page 185 Structure Removal......Page 187 Example 5.27......Page 189 Grand Tour......Page 191 Example 5.28......Page 195 5.5 MATLAB Code......Page 196 5.6 Further Reading......Page 197 Exercises......Page 200 6.1 Introduction......Page 204 Hypothesis Testing......Page 205 Example 6.1......Page 207 Example 6.2......Page 209 Example 6.3......Page 211 Example 6.4......Page 213 Confidence Intervals......Page 214 Example 6.5......Page 216 Basic Monte Carlo Procedure......Page 217 Monte Carlo Hypothesis Testing......Page 218 Example 6.6......Page 219 Example 6.7......Page 222 Monte Carlo Assessment of Hypothesis Testing......Page 223 Example 6.8......Page 224 General Bootstrap Methodology......Page 227 Bootstrap Estimate of Standard Error......Page 229 Example 6.9......Page 230 Bootstrap Estimate of Bias......Page 232 Bootstrap Standard Confidence Interval......Page 233 Bootstrap-t Confidence Interval......Page 234 Example 6.11......Page 235 Bootstrap Percentile Interval......Page 237 Example 6.12......Page 238 6.5 MATLAB Code......Page 239 6.6 Further Reading......Page 240 Exercises......Page 241 7.1 Introduction......Page 243 7.2 Cross- Validation......Page 244 Example 7.1......Page 246 Example 7.2......Page 248 Example 7.3......Page 250 7.3 Jackknife......Page 251 Example 7.4......Page 253 Example 7.5......Page 254 Example 7.6......Page 257 Example 7.7......Page 258 7.4 Better Bootstrap Confidence Intervals......Page 259 Example 7.8......Page 262 7.5 Jackknife- After- Bootstrap......Page 263 Example 7.9......Page 264 7.6 MATLAB Code......Page 265 7.7 Further Reading......Page 266 Exercises......Page 268 8.1 Introduction......Page 270 1-D Histograms......Page 272 Example 8.1......Page 274 Example 8.2......Page 277 Multivariate Histograms......Page 278 Frequency Polygons......Page 280 Example 8.3......Page 282 Example 8.4......Page 284 Averaged Shifted Histograms......Page 285 Example 8.5......Page 289 Univariate Kernel Estimators......Page 291 Example 8.6......Page 293 Multivariate Kernel Estimators......Page 296 Example 8.7......Page 297 8.4 Finite Mixtures......Page 298 Example 8.8......Page 300 Example 8.9......Page 302 Multivariate Finite Mixtures......Page 304 Example 8.10......Page 305 EM Algorithm for Estimating the Parameters......Page 307 Example 8.11......Page 310 Adaptive Mixtures......Page 312 Example 8.12......Page 315 8.5 Generating Random Variables......Page 317 Example 8.13......Page 320 8.7 Further Reading......Page 322 Exercises......Page 325 9.1 Introduction......Page 328 9.2 Bayes Decision Theory......Page 330 Example 9.1......Page 332 Example 9.2......Page 333 Bayes Decision Rule......Page 334 Example 9.3......Page 335 Example 9.4......Page 338 Likelihood Ratio Approach......Page 340 Example 9.5......Page 342 9.3 Evaluating the Classifier......Page 343 Example 9.6......Page 344 Example 9.7......Page 346 Receiver Operating Characteristic (ROC) Curve......Page 348 Example 9.8......Page 351 9.4 Classification Trees......Page 353 Example 9.9......Page 356 Example 9.10......Page 358 Example 9.11......Page 361 Pruning the Tree......Page 363 Example 9.12......Page 366 Choosing the Best Tree......Page 367 Selecting the Best Tree Using an Independent Test Sample......Page 368 Example 9.13......Page 371 Selecting the Best Tree Using Cross-Validation......Page 372 Example 9.14......Page 375 Measures of Distance......Page 378 Example 9.15......Page 379 Hierarchical Clustering......Page 380 Example 9.16......Page 382 K-Means Clustering......Page 384 Example 9.18......Page 386 9.6 MATLAB Code......Page 387 9.7 Further Reading......Page 390 Exercises......Page 392 10.1 Introduction......Page 395 Example 10.1......Page 396 Example 10.2......Page 398 10.2 Smoothing......Page 400 Loess......Page 401 Example 10.3......Page 403 Robust Loess Smoothing......Page 406 Example 10.4......Page 409 Upper and Lower Smooths......Page 410 10.3 Kernel Methods......Page 411 Nadaraya-Watson Estimator......Page 414 Local Linear Kernel Estimator......Page 415 10.4 Regression Trees......Page 417 Example 10.8......Page 420 Pruning a Regression Tree......Page 421 Selecting a Tree......Page 422 Example 10.9......Page 427 10.5 MATLAB Code......Page 429 10.6 Further Reading......Page 430 Exercises......Page 432 11.1 Introduction......Page 434 Bayesian Inference......Page 435 Monte Carlo Integration......Page 436 Example 11.1......Page 437 Markov Chains......Page 438 11.3 Metropolis- Hastings Algorithms......Page 439 Metropolis-Hastings Sampler......Page 440 Example 11.2......Page 441 Metropolis Sampler......Page 442 Example 11.3......Page 445 Independence Sampler......Page 447 Example 11.4......Page 448 Example 11.5......Page 450 11.4 The Gibbs Sampler......Page 452 Example 11.6......Page 454 Example 11.7......Page 456 Example 11.8......Page 459 11.5 Convergence Monitoring......Page 461 Gelman and Rubin Method......Page 462 Example 11.9......Page 465 11.6 MATLAB Code......Page 467 11.7 Further Reading......Page 470 Exercises......Page 471 What Is Spatial Statistics?......Page 474 Types of Spatial Data......Page 475 Spatial Point Patterns......Page 476 Complete Spatial Randomness......Page 478 Example 12.1......Page 480 Example 12.2......Page 481 Example 12.3......Page 482 Estimating the Intensity......Page 484 Example 12.4......Page 485 Nearest Neighbor Distances - G and F Distributions......Page 487 K-Function......Page 491 Example 12.5......Page 489 Example 12.6......Page 493 12.4 Modeling Spatial Point Processes......Page 494 Nearest Neighbor Distances......Page 495 Example 12.7......Page 498 K-Function......Page 499 Example 12.8......Page 501 Example 12.9......Page 502 Homogeneous Poisson Process......Page 504 Example 12.10......Page 505 Binomial Process......Page 506 Example 12.11......Page 507 Poisson Cluster Process......Page 508 Example 12.12......Page 509 Inhibition Process......Page 510 Example 12.13......Page 511 Example 12.14......Page 513 12.6 MATLAB Code......Page 514 12.7 Further Reading......Page 516 Exercises......Page 517 A. 1 What Is MATLAB?......Page 519 A. 3 File and Workspace Management......Page 520 A. 5 Arithmetic Operators......Page 522 A. 6 Data Constructs in MATLAB......Page 524 A. 7 Script Files and Functions......Page 526 A. 8 Control Flow......Page 528 A. 9 Simple Plotting......Page 529 A. 10 Contact Information......Page 532 Other......Page 533 Greek Symbols......Page 534 Acronyms......Page 535 Friedman-Tukey Index......Page 536 Moment Index......Page 537 L2 Distances......Page 538 C. 2 MATLAB Source Code......Page 539 D.1 Bootstrap Confidence Interval BCa......Page 545 D. 2 Adaptive Mixtures Density Estimation......Page 546 D. 3 Classification Trees......Page 548 D. 4 Regression Trees......Page 550 Appendix E: MATLAB Statistics Toolbox......Page 552 Computational Statistics Toolbox......Page 561 Appendix G: Data Sets......Page 566 References......Page 573