Textbook
An Introduction to Bootstrap Methods with Applications to RISBN: 9780470467046
240 pages
November 2011, ©2012

Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments.
The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods.
An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study.
Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upperundergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.
ACKNOWLEDGMENTS xv
LIST OF TABLES xvii
1 INTRODUCTION 1
1.1 Historical Background 1
1.2 Definition and Relationship to the Delta Method and Other Resampling Methods 3
1.2.1 Jackknife 6
1.2.2 Delta Method 7
1.2.3 CrossValidation 7
1.2.4 Subsampling 8
1.3 Wide Range of Applications 8
1.4 The Bootstrap and the R Language System 10
1.5 Historical Notes 25
1.6 Exercises 26
References 27
2 ESTIMATION 30
2.1 Estimating Bias 30
2.1.1 Bootstrap Adjustment 30
2.1.2 Error Rate Estimation in Discriminant Analysis 32
2.1.3 Simple Example of Linear Discrimination and Bootstrap Error Rate Estimation 42
2.1.4 Patch Data Example 51
2.2 Estimating Location 53
2.2.1 Estimating a Mean 53
2.2.2 Estimating a Median 54
2.3 Estimating Dispersion 54
2.3.1 Estimating an Estimate’s Standard Error 55
2.3.2 Estimating Interquartile Range 56
2.4 Linear Regression 56
2.4.1 Overview 56
2.4.2 Bootstrapping Residuals 57
2.4.3 Bootstrapping Pairs (Response and Predictor Vector) 58
2.4.4 Heteroscedasticity of Variance: The Wild Bootstrap 58
2.4.5 A Special Class of Linear Regression Models: Multivariable Fractional Polynomials 60
2.5 Nonlinear Regression 60
2.5.1 Examples of Nonlinear Models 61
2.5.2 A QuasiOptical Experiment 63
2.6 Nonparametric Regression 63
2.6.1 Examples of Nonparametric Regression Models 64
2.6.2 Bootstrap Bagging 66
2.7 Historical Notes 67
2.8 Exercises 69
References 71
3 CONFIDENCE INTERVALS 76
3.1 Subsampling, Typical Value Theorem, and Efron’s Percentile Method 77
3.2 Bootstrapt 79
3.3 Iterated Bootstrap 83
3.4 BiasCorrected (BC) Bootstrap 85
3.5 BCa and ABC 85
3.6 Tilted Bootstrap 88
3.7 Variance Estimation with Small Sample Sizes 90
3.8 Historical Notes 94
3.9 Exercises 96
References 98
4 HYPOTHESIS TESTING 101
4.1 Relationship to Confi dence Intervals 103
4.2 Why Test Hypotheses Differently? 105
4.3 Tendril DX Example 106
4.4 Klingenberg Example: Binary Dose–Response 108
4.5 Historical Notes 109
4.6 Exercises 110
References 111
5 TIME SERIES 113
5.1 Forecasting Methods 113
5.2 Time Domain Models 114
5.3 Can Bootstrapping Improve Prediction Intervals? 115
5.4 ModelBased Methods 118
5.4.1 Bootstrapping Stationary Autoregressive Processes 118
5.4.2 Bootstrapping Explosive Autoregressive Processes 123
5.4.3 Bootstrapping Unstable Autoregressive Processes 123
5.4.4 Bootstrapping Stationary ARMA Processes 123
5.5 Block Bootstrapping for Stationary Time Series 123
5.6 Dependent Wild Bootstrap (DWB) 126
5.7 FrequencyBased Approaches for Stationary Time Series 127
5.8 Sieve Bootstrap 128
5.9 Historical Notes 129
5.10 Exercises 131
References 131
6 BOOTSTRAP VARIANTS 136
6.1 Bayesian Bootstrap 137
6.2 Smoothed Bootstrap 138
6.3 Parametric Bootstrap 139
6.4 Double Bootstrap 139
6.5 The mOutofn Bootstrap 140
6.6 The Wild Bootstrap 141
6.7 Historical Notes 141
6.8 Exercises 142
References 142
7 CHAPTER SPECIAL TOPICS 144
7.1 Spatial Data 144
7.1.1 Kriging 144
7.1.2 Asymptotics for Spatial Data 147
7.1.3 Block Bootstrap on Regular Grids 148
7.1.4 Block Bootstrap on Irregular Grids 148
7.2 Subset Selection in Regression 148
7.2.1 Gong’s Logistic Regression Example 149
7.2.2 Gunter’s Qualitative Interaction Example 153
7.3 Determining the Number of Distributions in a Mixture 155
7.4 Censored Data 157
7.5 PValue Adjustment 158
7.5.1 The Westfall–Young Approach 159
7.5.2 Passive Plus Example 159
7.5.3 Consulting Example 160
7.6 Bioequivalence 162
7.6.1 Individual Bioequivalence 162
7.6.2 Population Bioequivalence 165
7.7 Process Capability Indices 165
7.8 Missing Data 172
7.9 Point Processes 174
7.10 Bootstrap to Detect Outliers 176
7.11 Lattice Variables 177
7.12 Covariate Adjustment of Area Under the Curve Estimates for Receiver Operating Characteristic (ROC) Curves 177
7.13 Bootstrapping in SAS 179
7.14 Historical Notes 182
7.15 Exercises 183
References 185
8 WHEN THE BOOTSTRAP IS INCONSISTENT AND HOW TO REMEDY IT 190
8.1 Too Small of a Sample Size 191
8.2 Distributions with Infi nite Second Moments 191
8.2.1 Introduction 191
8.2.2 Example of Inconsistency 192
8.2.3 Remedies 193
8.3 Estimating Extreme Values 194
8.3.1 Introduction 194
8.3.2 Example of Inconsistency 194
8.3.3 Remedies 194
8.4 Survey Sampling 195
8.4.1 Introduction 195
8.4.2 Example of Inconsistency 195
8.4.3 Remedies 195
8.5 mDependent Sequences 196
8.5.1 Introduction 196
8.5.2 Example of Inconsistency When Independence Is Assumed 196
8.5.3 Remedy 197
8.6 Unstable Autoregressive Processes 197
8.6.1 Introduction 197
8.6.2 Example of Inconsistency 197
8.6.3 Remedies 197
8.7 LongRange Dependence 198
8.7.1 Introduction 198
8.7.2 Example of Inconsistency 198
8.7.3 A Remedy 198
8.8 Bootstrap Diagnostics 199
8.9 Historical Notes 199
8.10 Exercises 201
References 201
AUTHOR INDEX 204
SUBJECT INDEX 210
MICHAEL R. CHERNICK, PhD, is Manager of Biostatistical Services at Lankenau Institute for Medical Research, where he conducts statistical design and analysis for pharmaceutical research. He has more than thirty years of experience in the application of statistical methods to such areas as medicine, energy, engineering, insurance, and pharmaceuticals. Dr. Chernick is the author of Bootstrap Methods: A Guide for Practitioners and Researchers, Second Edition and The Essentials of Biostatistics for Physicians, Nurses, and Clinicians, and the coauthor of Introductory Biostatistics for the Health Sciences: Modern Applications Including Bootstrap, all published by Wiley.
ROBERT A. LaBUDDE, PhD, is President of Least Cost Formulations, Ltd., a mathematical software development company that specializes in optimization and process control software for manufacturing companies. He has extensive experience in industry and academia and currently serves as Adjunct Associate Professor in the Department of Mathematics and Statistics at Old Dominion University.
 The book includes a listing of the most extensive and detailed bootstrap bibliography available on the market, including more than 2,500 references.
 Solutions to bootstrap failures are presented, such as finite populations and extreme values.
 Historical notes at the end of each chapter, as well as extensive illustrations, charts and tables and showcased throughout the book.
 Great care is given to craft smooth transitions amongst chapters and between sections.
 Examples and explanations of when and why the bootstrap is not effective are presented for learning and comparative purposes.
 Discussions of applications to a wide variety of confidence intervals, hypothesis testing, and estimation problems (including pharmaceutical, genomics, and economics) are given, oftentimes at the expense of detailed explanations and/or needless theoretical justification.
 R subroutines are illustrated throughout the presentation and redeployed on an authormaintained web site, along with helpful hints to more than 300 exercises in the book.
An Introduction to Bootstrap Methods with Applications to R (US $114.00)
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