Computer Science and Data Analysis Series Computational Statistics Handbook with MATLAB® Second Edition Wendy L. Martinez The Office of Naval Research Arlington, Virginia, U.S.A. Angel R. Martinez Naval Surface Warfare Center Dahlgren, Virginia, U.S.A. Chapman &. Hall/CRC Taylor & Francis Group Boca Raton London New York «H Chapman & Hall/CRC is an imprint of the Taylor & Francis Group, an informa business
Libro practico para el desarrollo y analisis estadisticos utilizando el programa Matlab, muy recomendado para estudiantes y profesionales de ingenieria y economia y ciencias afines.
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Computer Science and Data Analysis Series
Computational Statistics Handbook
with MATLAB® Second Edition
Wendy L. Martinez The Office of Naval Research
Arlington, Virginia, U.S.A.
Angel R. Martinez Naval Surface Warfare Center
Dahlgren, Virginia, U.S.A.
Chapman &. Hall/CRC Taylor & Francis Group
Boca Raton London New York
«H
Chapman & Hall/CRC is an imprint of the Taylor & Francis Group, an informa business
Table ofContents
Preface to the Second Edition xvii Preface to the First Edition xxi
Chapter 1 Introduction 1.1 What Is Computational Statistics? 1 1.2 An Overview of the Book 2
Philosophy 2 What Is Covered 3 A Word About Notation 5
6.8 MATLAB® Code 224 6.9 Further Reading 227 Exercises 230
Chapter 7 Monte Carlo Methods for Inferential Statistics 7.1 Introduction 233 7.2 Classical Inferential Statistics 234
Hypothesis Testing 234 Confidence Intervals 243
7.3 Monte Carlo Methods for Inferential Statistics 246 Basic Monte Carlo Procedure 246 Monte Carlo Hypothesis Testing 247 Monte Carlo Assessment of Hypothesis Testing 252
7.4 Bootstrap Methods 256 General Bootstrap Methodology 256 Bootstrap Estimate of Standard Error 258 Bootstrap Estimate of Bias 260 Bootstrap Confidence Intervals 262
7.5 MATLAB® Code 268 7.6 Further Reading 269 Exercises 271
Finite Mixture Models and the EM Algorithm 446 Model-Based Agglomerative Clustering 450 Bayesian Information Criterion 453 Model-Based Clustering Procedure 453
11.6 Assessing Cluster Results 458 Mojena - Upper Tail Rule 458 Silhouette Statistic 459 Other Methods for Evaluating Clusters 462
11.7 MATLAB® Code 465 11.8 Further Reading 466 Exercises 469
Running Line 518 Local Polynomial Regression - Loess 519 Robust Loess 525
13.3 Kernel Methods 528 Nadaraya-Watson Estimator 531 Local Linear Kernel Estimator 532
13.4 Smoothing Splines 534 Natural Cubic Splines 536 Reinsch Method for Finding Smoothing Splines 537 Values for a Cubic Smoothing Spline 540 Weighted Smoothing Spline 540
13.5 Nonparametric Regression - Other Details 542 Choosing the Smoothing Parameter 542 Estimation of the Residual Variance 547 Variability of Smooths 548
13.6 Regression Trees 551 Growing a Regression Tree 553 Pruning a Regression Tree 557 Selecting a Tree 557
15.5 Simulating Spatial Point Processes 646 Homogeneous Poisson Process 647 Binomial Process 650 Poisson Cluster Process 651 Inhibition Process 654 Strauss Process 656
15.6 MATLAB® Code 658 15.7 Further Reading 659 Exercises 661
Appendix A Introduction to MATLAB® A.l What Is MATLAB®? 663 A.2 Getting Help in MATLAB® 664 A.3 File and Workspace Management 664 A.4 Punctuation in MATLAB® 666 A.5 Arithmetic Operators 666 A.6 Data Constructs in MATLAB® 668
Basic Data Constructs 668 Building Arrays 668 CellArrays 669
A.7 Script Files and Functions 670 A.8 Control Flow 672
A.9 Simple Plotting 673 A.10 Contact Information 676
Table ofContents xv
Appendix B Projection Pursuit Indexes B.l Indexes 677
Friedman-Tukey Index 677 Entropy Index 678 Moment Index 678 L2Distances 679
B.2 MATLAB® Source Code 680
Appendix C MATLAB® Statistics Toolbox File I /O 687 Dataset Arrays 687 GroupedData 687 Descriptive Statistics 688 Statistical Visualization 688 Probability Density Functions 689 Cumulative Distribution Functions 690 Inverse Cumulative Distribution Functions 691 Distribution Statistics Functions 691 Distribution Fitting Functions 692 Negative Log-Likelihood Functions 692 Random Number Generators 693 Hypothesis Tests 694 Analysis of Variance 694 Regression Analysis 694 Multivariate Methods 695 Cluster Analysis 696 Classification 696 Markov Models 696 Design of Experiments 697 Statistical Process Control 697 Graphical User Interfaces 697
A p p e n d i x D Computational Statistics Toolbox Probability Distributions 699 Statistics 699 Random Number Generation 700 Exploratory Data Analysis 700 Bootstrap and Jackknife 701 Probability Density Estimation 701 Supervised Learning 701 Unsupervised Learning 701
xvi Computational Statistics Handbook with MATLAB®, 2ND Edition
Parametric and Nonparametric Models 702 Markov Chain Monte Carlo 702 Spatial Statistics 702
Appendix E Exploratory Data Analysis Toolboxes E.l Introduction 703 E.2 Exploratory Data Analysis Toolbox 704 E.3 EDA GUI Toolbox 705
Appendix F Data Sets Introduction 719
Appendix G Notation Overview 727 ObservedData 727 Greek Letters 728 Functions and Distributions 728 Matrix Notation 729 Statistics 729