【下载】Bootstrap Methods 权威著作

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dingfu294 发表于 2008-4-30 23:54:00 |AI写论文

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Bootstrap 方法经典著作Chernick 著388页英文版非影印、清晰版Contents Preface to Second Edition&nbsp; ix Preface to First Edition&nbsp; xiii Acknowledgments xvii 1. What Is Bootstrapping? 1 &nbsp;1.1. Background, 1 &nbsp;1.2. Introduction, 8 &nbsp; 1.3.&nbsp; Wide Range of Applications,&nbsp; 13 &nbsp;1.4. Historical Notes, 16 &nbsp;1.5. Summary, 24 2. Estimation 26 &nbsp;2.1. Estimating Bias, 26 &nbsp;&nbsp;&nbsp; 2.1.1.&nbsp; How to Do It by Bootstrapping,&nbsp; 26 &nbsp;&nbsp;&nbsp; 2.1.2.&nbsp; Error Rate Estimation in Discrimination,&nbsp; 28 &nbsp;&nbsp;&nbsp; 2.1.3.&nbsp; Error Rate Estimation: An Illustrative Problem,&nbsp; 39 &nbsp;&nbsp;&nbsp; 2.1.4.&nbsp; Efron’s Patch Data Example,&nbsp; 44 &nbsp; 2.2.&nbsp; Estimating Location and Dispersion,&nbsp; 46 &nbsp;&nbsp; 2.2.1. Means and Medians, 47 &nbsp;&nbsp;&nbsp; 2.2.2.&nbsp; Standard Errors and Quartiles,&nbsp; 48 &nbsp;2.3. Historical Notes, 51 3. Conﬁ&nbsp; dence Sets and Hypothesis Testing 53 &nbsp;3.1. Conﬁ&nbsp; dence Sets,&nbsp; 55 &nbsp;&nbsp;&nbsp; 3.1.1.&nbsp; Typical Value Theorems for M-Estimates,&nbsp; 55 &nbsp;&nbsp; 3.1.2. Percentile Method, 57vi contents &nbsp;&nbsp;&nbsp; 3.1.3.&nbsp; Bias Correction and the Acceleration Constant,&nbsp; 58 &nbsp;&nbsp; 3.1.4. Iterated Bootstrap, 61 &nbsp;&nbsp; 3.1.5. Bootstrap Percentile t Conﬁ&nbsp; dence Intervals,&nbsp; 64 &nbsp; 3.2.&nbsp;&nbsp; Relationship Between Conﬁ&nbsp; dence Intervals and Tests of Hypotheses, 64 &nbsp;3.3. Hypothesis Testing Problems, 66 &nbsp;&nbsp;&nbsp; 3.3.1.&nbsp; Tendril DX Lead Clinical Trial Analysis,&nbsp; 67 &nbsp; 3.4.&nbsp;&nbsp; An Application of Bootstrap Conﬁ&nbsp; dence Intervals to Binary Dose–Response Modeling,&nbsp; 71 &nbsp;3.5.&nbsp; Historical Notes, 75 4. Regression Analysis 78 &nbsp;4.1.&nbsp; Linear Models, 82 &nbsp;&nbsp; 4.1.1.&nbsp; Gauss–Markov Theory, 83 &nbsp;&nbsp;&nbsp; 4.1.2.&nbsp;&nbsp; Why Not Just Use Least Squares?&nbsp; 83 &nbsp;&nbsp;&nbsp; 4.1.3.&nbsp;&nbsp; Should I Bootstrap the Residuals from the Fit?&nbsp; 84 &nbsp;4.2.&nbsp; Nonlinear Models, 86 &nbsp;&nbsp;&nbsp; 4.2.1.&nbsp;&nbsp; Examples of Nonlinear Models,&nbsp; 87 &nbsp;&nbsp;&nbsp; 4.2.2.&nbsp;&nbsp; A Quasi-optical Experiment,&nbsp; 89 &nbsp;4.3.&nbsp; Nonparametric Models, 93 &nbsp;4.4.&nbsp; Historical Notes, 94 5. Forecasting and Time Series Analysis 97 &nbsp; 5.1.&nbsp;&nbsp; Methods of Forecasting,&nbsp; 97 &nbsp; 5.2.&nbsp;&nbsp; Time Series Models,&nbsp; 98 &nbsp; 5.3.&nbsp;&nbsp; When Does Bootstrapping Help with Prediction Intervals?&nbsp; 99 &nbsp; 5.4.&nbsp;&nbsp; Model-Based Versus Block Resampling,&nbsp; 103 &nbsp; 5.5.&nbsp;&nbsp; Explosive Autoregressive Processes,&nbsp; 107 &nbsp; 5.6.&nbsp;&nbsp; Bootstrapping-Stationary Arma Models,&nbsp; 108 &nbsp;5.7.&nbsp; Frequency-Based Approaches, 108 &nbsp;5.8.&nbsp; Sieve Bootstrap, 110 &nbsp;5.9.&nbsp; Historical Notes, 111 6. Which Resampling Method Should You Use? 114 &nbsp;6.1.&nbsp; Related Methods, 115 &nbsp;&nbsp; 6.1.1.&nbsp; Jackknife, 115 &nbsp;&nbsp;&nbsp; 6.1.2.&nbsp;&nbsp; Delta Method, Inﬁ&nbsp; nitesimal Jackknife, and Inﬂ&nbsp; uence Functions, 116 &nbsp;&nbsp; 6.1.3.&nbsp; Cross-Validation, 119 &nbsp;&nbsp; 6.1.4.&nbsp; Subsampling, 119contents vii &nbsp;6.2.&nbsp; Bootstrap Variants, 120 &nbsp;&nbsp; 6.2.1.&nbsp; Bayesian Bootstrap, 121 &nbsp;&nbsp;&nbsp; 6.2.2.&nbsp;&nbsp; The Smoothed Boostrap,&nbsp; 123 &nbsp;&nbsp;&nbsp; 6.2.3.&nbsp;&nbsp; The Parametric Bootstrap,&nbsp; 124 &nbsp;&nbsp; 6.2.4.&nbsp; Double Bootstrap, 125 &nbsp;&nbsp;&nbsp; 6.2.5.&nbsp;&nbsp; The m-out-of-n Bootstrap,&nbsp; 125 7. Efﬁ&nbsp; cient and Effective Simulation 127 &nbsp; 7.1.&nbsp;&nbsp; How Many Replications?&nbsp; 128 &nbsp; 7.2.&nbsp;&nbsp; Variance Reduction Methods,&nbsp; 129 &nbsp;&nbsp; 7.2.1.&nbsp; Linear Approximation, 129 &nbsp;&nbsp; 7.2.2.&nbsp; Balanced Resampling, 131 &nbsp;&nbsp; 7.2.3.&nbsp; Antithetic Variates, 132 &nbsp;&nbsp; 7.2.4.&nbsp; Importance Sampling, 133 &nbsp;&nbsp; 7.2.5.&nbsp; Centering, 134 &nbsp; 7.3.&nbsp;&nbsp; When Can Monte Carlo Be Avoided?&nbsp; 135 &nbsp;7.4.&nbsp; Historical Notes, 136 8. Special Topics 1 3 9 &nbsp;8.1.&nbsp; Spatial Data, 139 &nbsp;&nbsp; 8.1.1.&nbsp; Kriging, 139 &nbsp;&nbsp;&nbsp; 8.1.2.&nbsp;&nbsp; Block Bootstrap on Regular Grids,&nbsp; 142 &nbsp;&nbsp;&nbsp; 8.1.3.&nbsp;&nbsp; Block Bootstrap on Irregular Grids,&nbsp; 143 &nbsp;8.2.&nbsp; Subset Selection, 143 &nbsp; 8.3.&nbsp;&nbsp; Determining the Number of Distributions in a Mixture Model, 145 &nbsp;8.4.&nbsp; Censored Data, 148 &nbsp;8.5.&nbsp;&nbsp; p-Value Adjustment,&nbsp; 149 &nbsp;&nbsp;&nbsp; 8.5.1.&nbsp;&nbsp; Description of Westfall–Young Approach,&nbsp; 150 &nbsp;&nbsp;&nbsp; 8.5.2.&nbsp;&nbsp; Passive Plus DX Example,&nbsp; 150 &nbsp;&nbsp; 8.5.3.&nbsp; Consulting Example, 152 &nbsp;8.6.&nbsp; Bioequivalence Applications, 153 &nbsp;&nbsp; 8.6.1.&nbsp; Individual Bioequivalence, 153 &nbsp;&nbsp; 8.6.2.&nbsp; Population Bioequivalence, 155 &nbsp; 8.7.&nbsp;&nbsp; Process Capability Indices,&nbsp; 156 &nbsp;8.8.&nbsp; Missing Data, 164 &nbsp;8.9.&nbsp; Point Processes, 166 &nbsp;8.10.&nbsp; Lattice Variables, 168 &nbsp;8.11.&nbsp; Historical Notes, 169viii contents 9. When Bootstrapping Fails Along with Remedies for Failures 172 &nbsp; 9.1.&nbsp;&nbsp; Too Small of a Sample Size,&nbsp; 173 &nbsp; 9.2.&nbsp;&nbsp; Distributions with Inﬁ&nbsp; nite Moments,&nbsp; 175 &nbsp;&nbsp; 9.2.1.&nbsp; Introduction, 175 &nbsp;&nbsp;&nbsp; 9.2.2.&nbsp;&nbsp; Example of Inconsistency,&nbsp; 176 &nbsp;&nbsp; 9.2.3.&nbsp; Remedies, 176 &nbsp; 9.3.&nbsp;&nbsp; Estimating Extreme Values,&nbsp; 177 &nbsp;&nbsp; 9.3.1.&nbsp; Introduction, 177 &nbsp;&nbsp; 9.3.2. Example of Inconsistency, 177 &nbsp;&nbsp; 9.3.3. Remedies, 178 &nbsp;9.4.&nbsp; Survey Sampling, 179 &nbsp;&nbsp; 9.4.1.&nbsp; Introduction, 179 &nbsp;&nbsp; 9.4.2. Example of Inconsistency, 180 &nbsp;&nbsp; 9.4.3. Remedies, 180 &nbsp; 9.5.&nbsp;&nbsp; Data Sequences that Are M-Dependent, 180 &nbsp;&nbsp; 9.5.1.&nbsp; Introduction, 180 &nbsp;&nbsp; 9.5.2.&nbsp;&nbsp; Example of Inconsistency When Independence Is Assumed, 181 &nbsp;&nbsp; 9.5.3. Remedies, 181 &nbsp; 9.6.&nbsp;&nbsp; Unstable Autoregressive Processes,&nbsp; 182 &nbsp;&nbsp; 9.6.1.&nbsp; Introduction, 182 &nbsp;&nbsp;&nbsp; 9.6.2.&nbsp;&nbsp; Example of Inconsistency,&nbsp; 182 &nbsp;&nbsp; 9.6.3.&nbsp; Remedies, 183 &nbsp;9.7.&nbsp; Long-Range Dependence, 183 &nbsp;&nbsp; 9.7.1.&nbsp; Introduction, 183 &nbsp;&nbsp;&nbsp; 9.7.2.&nbsp;&nbsp; Example of Inconsistency,&nbsp; 183 &nbsp;&nbsp; 9.7.3.&nbsp; Remedies, 184 &nbsp;9.8.&nbsp; Bootstrap Diagnostics, 184 &nbsp;9.9.&nbsp; Historical Notes, 185 Bibliography 1 (Prior to 1999) 188 Bibliography 2 (1999–2007) 274 Author Index 330 Subject Index

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dingfu294(未真实交易用户) 发表于 2008-5-1 00:06:00

Bootstrap 方法经典著作

Chernick 著

388页英文版非影印、清晰版

Contents
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, 57vi 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, 119contents 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 1 3 9
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, 169viii 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

藤椅

dingfu295(真实交易用户) 发表于 2008-5-1 00:06:00

Bootstrap 方法经典著作

[此贴子已经被作者于2008-5-1 0:07:33编辑过]

板凳

强功(未真实交易用户) 发表于 2008-5-1 00:18:00

好书，一定得顶~~~谢谢楼主

报纸

catch007(真实交易用户) 发表于 2008-6-7 01:12:00

碰到好书，一定要顶啊

地板

zst112all(未真实交易用户) 发表于 2008-6-7 09:11:00

很好的书，楼主的目录很详细！赞

不要笑话我，俺就是瘪四！

7楼

smilehu(未真实交易用户) 发表于 2008-6-8 22:24:00

好书可惜没钱

知我者谓我心忧，不知我者谓我何求

8楼

alantin(真实交易用户) 发表于 2008-6-9 01:13:00

Ding

9楼

robinhq(真实交易用户) 发表于 2008-6-24 17:26:00

bootstrap 的书也读过几本了，这本不知道为什么比图书馆的厚呢？所以偶就买了瞅瞅

10楼

xiaobei_2000(未真实交易用户) 发表于 2008-6-25 09:07:00

请问大家一般将bootstrap都用在什么地方？

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