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