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【书名】 Bootstrap Methods: A Guide for Practitioners and Researchers
【作者】MICHAEL R. CHERNICK
【出版社】2008 by John Wiley & Sons, Inc.
【版本】2
【出版日期】2008
【文件格式】PDF，

【文件大小】2.13Mb in .rar
【页数】369
【ISBN出版号】ISBN 978-0-471-75621-7 (cloth)
【资料类别】统计学，textbook

【市面定价】103 usd
【扫描版还是影印版】original
【是否缺页】complete

【关键词】bootstrap, resampling
【内容简介】This book can serve as a useful resource when trying to locate research papers on bootstrap methods and specific applications and will enhance understanding of the historical development of this important area of modern statistics.

【目录】

Preface to Second Edition ix
Preface to First Edition xiii
Acknowledgments xvii
1. What Is Bootstrapping? 1
1.1. Background, 1
1.2. Introduction, 8
1.3. Wide Range of Applications, 13
1.4. Historical Notes, 16
1.5. Summary, 24
2. Estimation 26
2.1. Estimating Bias, 26
2.1.1. How to Do It by Bootstrapping, 26
2.1.2. Error Rate Estimation in Discrimination, 28
2.1.3. Error Rate Estimation: An Illustrative Problem, 39
2.1.4. Efron’s Patch Data Example, 44
2.2. Estimating Location and Dispersion, 46
2.2.1. Means and Medians, 47
2.2.2. Standard Errors and Quartiles, 48
2.3. Historical Notes, 51
3. Confi dence Sets and Hypothesis Testing 53
3.1. Confi dence Sets, 55
3.1.1. Typical Value Theorems for M-Estimates, 55
3.1.2. Percentile Method, 57
vi contents
3.1.3. Bias Correction and the Acceleration Constant, 58
3.1.4. Iterated Bootstrap, 61
3.1.5. Bootstrap Percentile t Confi dence Intervals, 64
3.2. Relationship Between Confi dence Intervals and Tests of
Hypotheses, 64
3.3. Hypothesis Testing Problems, 66
3.3.1. Tendril DX Lead Clinical Trial Analysis, 67
3.4. An Application of Bootstrap Confi dence Intervals to Binary
Dose–Response Modeling, 71
3.5. Historical Notes, 75
4. Regression Analysis 78
4.1. Linear Models, 82
4.1.1. Gauss–Markov Theory, 83
4.1.2. Why Not Just Use Least Squares? 83
4.1.3. Should I Bootstrap the Residuals from the Fit? 84
4.2. Nonlinear Models, 86
4.2.1. Examples of Nonlinear Models, 87
4.2.2. A Quasi-optical Experiment, 89
4.3. Nonparametric Models, 93
4.4. Historical Notes, 94
5. Forecasting and Time Series Analysis 97
5.1. Methods of Forecasting, 97
5.2. Time Series Models, 98
5.3. When Does Bootstrapping Help with Prediction Intervals? 99
5.4. Model-Based Versus Block Resampling, 103
5.5. Explosive Autoregressive Processes, 107
5.6. Bootstrapping-Stationary Arma Models, 108
5.7. Frequency-Based Approaches, 108
5.8. Sieve Bootstrap, 110
5.9. Historical Notes, 111
6. Which Resampling Method Should You Use? 114
6.1. Related Methods, 115
6.1.1. Jackknife, 115
6.1.2. Delta Method, Infi nitesimal Jackknife, and Infl uence
Functions, 116
6.1.3. Cross-Validation, 119
6.1.4. Subsampling, 119
contents vii
6.2. Bootstrap Variants, 120
6.2.1. Bayesian Bootstrap, 121
6.2.2. The Smoothed Boostrap, 123
6.2.3. The Parametric Bootstrap, 124
6.2.4. Double Bootstrap, 125
6.2.5. The m-out-of-n Bootstrap, 125
7. Effi cient and Effective Simulation 127
7.1. How Many Replications? 128
7.2. Variance Reduction Methods, 129
7.2.1. Linear Approximation, 129
7.2.2. Balanced Resampling, 131
7.2.3. Antithetic Variates, 132
7.2.4. Importance Sampling, 133
7.2.5. Centering, 134
7.3. When Can Monte Carlo Be Avoided? 135
7.4. Historical Notes, 136
8. Special Topics 139
8.1. Spatial Data, 139
8.1.1. Kriging, 139
8.1.2. Block Bootstrap on Regular Grids, 142
8.1.3. Block Bootstrap on Irregular Grids, 143
8.2. Subset Selection, 143
8.3. Determining the Number of Distributions in a Mixture
Model, 145
8.4. Censored Data, 148
8.5. p-Value Adjustment, 149
8.5.1. Description of Westfall–Young Approach, 150
8.5.2. Passive Plus DX Example, 150
8.5.3. Consulting Example, 152
8.6. Bioequivalence Applications, 153
8.6.1. Individual Bioequivalence, 153
8.6.2. Population Bioequivalence, 155
8.7. Process Capability Indices, 156
8.8. Missing Data, 164
8.9. Point Processes, 166
8.10. Lattice Variables, 168
8.11. Historical Notes, 169
viii contents
9. When Bootstrapping Fails Along with Remedies for Failures 172
9.1. Too Small of a Sample Size, 173
9.2. Distributions with Infi nite Moments, 175
9.2.1. Introduction, 175
9.2.2. Example of Inconsistency, 176
9.2.3. Remedies, 176
9.3. Estimating Extreme Values, 177
9.3.1. Introduction, 177
9.3.2. Example of Inconsistency, 177
9.3.3. Remedies, 178
9.4. Survey Sampling, 179
9.4.1. Introduction, 179
9.4.2. Example of Inconsistency, 180
9.4.3. Remedies, 180
9.5. Data Sequences that Are M-Dependent, 180
9.5.1. Introduction, 180
9.5.2. Example of Inconsistency When Independence Is
Assumed, 181
9.5.3. Remedies, 181
9.6. Unstable Autoregressive Processes, 182
9.6.1. Introduction, 182
9.6.2. Example of Inconsistency, 182
9.6.3. Remedies, 183
9.7. Long-Range Dependence, 183
9.7.1. Introduction, 183
9.7.2. Example of Inconsistency, 183
9.7.3. Remedies, 184
9.8. Bootstrap Diagnostics, 184
9.9. Historical Notes, 185
Bibliography 1 (Prior to 1999) 188
Bibliography 2 (1999–2007) 274
Author Index 330
Subject Index 359
【原创书评】

這本新版书从屴史與動机介紹了bootstrap的起源, 詳細的闡明了這一門統計工具的發展過程,从一開始就接觸到应用面. 文笔可算力求观念清晰,初看頁數不算頂多,但因為旁徵博引,实質內容可算庬大而且读者需要相當数理統計基礎.相比之下 resampling:new statistics 比較省力

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