Bootstrap Methods: A Guide for Practitioners and Researchers (Wiley Series in Probability and Statistics)

✍ Scribed by Michael R. Chernick

• Year: 2007
• Tongue: English
• Leaves: 400
• Edition: 2
• Category: Library

✦ Synopsis

A practical and accessible introduction to the bootstrap method——newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features: The addition of a second, extended bibliography devoted solely to publications from 1999–2007, which is a valuable collection of references on the latest research in the field A discussion of the new areas of applicability for bootstrap methods, including use in the pharmaceutical industry for estimating individual and population bioequivalence in clinical trials A revised chapter on when and why bootstrap fails and remedies for overcoming these drawbacks Added coverage on regression, censored data applications, P-value adjustment, ratio estimators, and missing data New examples and illustrations as well as extensive historical notes at the end of each chapter With a strong focus on application, detailed explanations of methodology, and complete coverage of modern developments in the field, Bootstrap Methods, Second Edition is an indispensable reference for applied statisticians, engineers, scientists, clinicians, and other practitioners who regularly use statistical methods in research. It is also suitable as a supplementary text for courses in statistics and resampling methods at the upper-undergraduate and graduate levels.

✦ Table of Contents

Front Cover......Page 4
Title......Page 6
Copyright......Page 7
Contents......Page 8
Preface to Second Edition......Page 12
Preface to First Edition......Page 16
Acknowledgments......Page 20
1.1. Background......Page 22
1.2. Introduction......Page 29
1.3. Wide Range of Applications......Page 34
1.4. Historical Notes......Page 37
1.5. Summary......Page 45
2.1.1. How to Do It by Bootstrapping......Page 47
2.1.2. Error Rate Estimation in Discrimination......Page 49
2.1.3. Error Rate Estimation: An Illustrative Problem......Page 60
2.1.4. Efron’s Patch Data Example......Page 65
2.2. Estimating Location and Dispersion......Page 67
2.2.1. Means and Medians......Page 68
2.2.2. Standard Errors and Quartiles......Page 69
2.3. Historical Notes......Page 72
3. Confidence Sets and Hypothesis Testing......Page 74
3.1.1. Typical Value Theorems for M-Estimates......Page 76
3.1.2. Percentile Method......Page 78
3.1.3. Bias Correction and the Acceleration Constant......Page 79
3.1.4. Iterated Bootstrap......Page 82
3.2. Relationship Between Confidence Intervals and Tests of Hypotheses......Page 85
3.3. Hypothesis Testing Problems......Page 87
3.3.1. Tendril DX Lead Clinical Trial Analysis......Page 88
3.4. An Application of Bootstrap Confidence Intervals to Binary Dose–Response Modeling......Page 92
3.5. Historical Notes......Page 96
4. Regression Analysis......Page 99
4.1. Linear Models......Page 103
4.1.2. Why Not Just Use Least Squares?......Page 104
4.1.3. Should I Bootstrap the Residuals from the Fit?......Page 105
4.2. Nonlinear Models......Page 107
4.2.1. Examples of Nonlinear Models......Page 108
4.2.2. A Quasi-optical Experiment......Page 110
4.3. Nonparametric Models......Page 114
4.4. Historical Notes......Page 115
5.1. Methods of Forecasting......Page 118
5.2. Time Series Models......Page 119
5.3. When Does Bootstrapping Help with Prediction Intervals?......Page 120
5.4. Model-Based Versus Block Resampling......Page 124
5.5. Explosive Autoregressive Processes......Page 128
5.7. Frequency-Based Approaches......Page 129
5.8. Sieve Bootstrap......Page 131
5.9. Historical Notes......Page 132
6. Which Resampling Method Should You Use?......Page 135
6.1.1. Jackknife......Page 136
6.1.2. Delta Method, Infinitesimal Jackknife, and Influence Functions......Page 137
6.1.4. Subsampling......Page 140
6.2. Bootstrap Variants......Page 141
6.2.1. Bayesian Bootstrap......Page 142
6.2.2. The Smoothed Boostrap......Page 144
6.2.3. The Parametric Bootstrap......Page 145
6.2.5. The m-out-of-n Bootstrap......Page 146
7. Efficient and Effective Simulation......Page 148
7.1. How Many Replications?......Page 149
7.2.1. Linear Approximation......Page 150
7.2.2. Balanced Resampling......Page 152
7.2.3. Antithetic Variates......Page 153
7.2.4. Importance Sampling......Page 154
7.2.5. Centering......Page 155
7.3. When Can Monte Carlo Be Avoided?......Page 156
7.4. Historical Notes......Page 157
8.1.1. Kriging......Page 160
8.1.2. Block Bootstrap on Regular Grids......Page 163
8.2. Subset Selection......Page 164
8.3. Determining the Number of Distributions in a Mixture Model......Page 166
8.4. Censored Data......Page 169
8.5. p-Value Adjustment......Page 170
8.5.2. Passive Plus DX Example......Page 171
8.5.3. Consulting Example......Page 173
8.6.1. Individual Bioequivalence......Page 174
8.6.2. Population Bioequivalence......Page 176
8.7. Process Capability Indices......Page 177
8.8. Missing Data......Page 185
8.9. Point Processes......Page 187
8.10. Lattice Variables......Page 189
8.11. Historical Notes......Page 190
9. When Bootstrapping Fails Along with Remedies for Failures......Page 193
9.1. Too Small of a Sample Size......Page 194
9.2.1. Introduction......Page 196
9.2.3. Remedies......Page 197
9.3.2. Example of Inconsistency......Page 198
9.3.3. Remedies......Page 199
9.4.1. Introduction......Page 200
9.5.1. Introduction......Page 201
9.5.3. Remedies......Page 202
9.6.2. Example of Inconsistency......Page 203
9.7.2. Example of Inconsistency......Page 204
9.8. Bootstrap Diagnostics......Page 205
9.9. Historical Notes......Page 206
Bibliography 1 (Prior to 1999)......Page 209
Bibliography 2 (1999–2007)......Page 295
Author Index......Page 351
Subject Index......Page 380