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

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