The jackknife and bootstrap are the most popular data-resampling meth ods used in statistical analysis. The resampling methods replace theoreti cal derivations required in applying traditional methods (such as substitu tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples. Because of the availability of inexpensive and fast computing, these computer-intensive methods have caught on very rapidly in recent years and are particularly appreciated by applied statisticians. The primary aims of this book are (1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians: practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further devel opments of the resampling methods. The theoretical properties of the jackknife and bootstrap methods are studied in this book in an asymptotic framework. Theorems are illustrated by examples. Finite sample properties of the jackknife and bootstrap are mostly investigated by examples and/or empirical simulation studies. In addition to the theory for the jackknife and bootstrap methods in problems with independent and identically distributed (Li.d.) data, we try to cover, as much as we can, the applications of the jackknife and bootstrap in various complicated non-Li.d. data problems.

Autorentext

Jun Shao is Professor of Statistics at the University of Wisconsin, Madison.

Klappentext

The Jackknife and bootstrap are the most popular data-resampling methods used in statistical analysis. This book provides a systematic introduction to the theory of the jackknife, bootstrap and other resampling methods that have been developed in the last twenty years. It aims to provide a guide to using these methods which will enable applied statisticians to feel comfortable in applying them to data in their own research. The authors have included examples of applying these methods in various applications in both the independent and identically distributed (iid) case and in more complicated cases with non-iid data sets. Readers are assumed to have a reasonable knowledge of mathematical statistics and so this will be made suitable reading for graduate students, researchers and practitioners seeking a wide-ranging survey of this important area of statistical theory and application.

Inhalt

1. Introduction.- 1.1 Statistics and Their Sampling Distributions.- 1.2 The Traditional Approach.- 1.3 The Jackknife.- 1.4 The Bootstrap.- 1.5 Extensions to Complex Problems.- 1.6 Scope of Our Studies.- 2. Theory for the Jackknife.- 2.1 Variance Estimation for Functions of Means.- 2.2 Variance Estimation for Functionals.- 2.3 The Delete-d Jackknife.- 2.4 Other Applications.- 2.5 Conclusions and Discussions.- 3. Theory for the Bootstrap.- 3.1 Techniques in Proving Consistency.- 3.2 Consistency: Some Major Results.- 3.3 Accuracy and Asymptotic Comparisons.- 3.4 Fixed Sample Performance.- 3.5 Smoothed Bootstrap.- 3.6 Nonregular Cases.- 3.7 Conclusions and Discussions.- 4. Bootstrap Confidence Sets and Hypothesis Tests.- 4.1 Bootstrap Confidence Sets.- 4.2 Asymptotic Theory.- 4.3 The Iterative Bootstrap and Other Methods.- 4.4 Empirical Comparisons.- 4.5 Bootstrap Hypothesis Tests.- 4.6 Conclusions and Discussions.- 5. Computational Methods.- 5.1 The Delete-1 Jackknife.- 5.2 The Delete-d Jackknife.- 5.3 Analytic Approaches for the Bootstrap.- 5.4 Simulation Approaches for the Bootstrap.- 5.5 Conclusions and Discussions.- 6. Applications to Sample Surveys.- 6.1 Sampling Designs and Estimates.- 6.2 Resampling Methods.- 6.3 Comparisons by Simulation.- 6.4 Asymptotic Results.- 6.5 Resampling Under Imputation.- 6.6 Conclusions and Discussions.- 7. Applications to Linear Models.- 7.1 Linear Models and Regression Estimates.- 7.2 Variance and Bias Estimation.- 7.3 Inference and Prediction Using the Bootstrap.- 7.4 Model Selection.- 7.5 Asymptotic Theory.- 7.6 Conclusions and Discussions.- 8. Applications to Nonlinear, Nonparametric, and Multivariate Models.- 8.1 Nonlinear Regression.- 8.2 Generalized Linear Models.- 8.3 Cox's Regression Models.- 8.4 Kernel Density Estimation.-8.5 Nonparametric Regression.- 8.6 Multivariate Analysis.- 8.7 Conclusions and Discussions.- 9. Applications to Time Series and Other Dependent Data.- 9.1 m-Dependent Data.- 9.2 Markov Chains.- 9.3 Autoregressive Time Series.- 9.4 Other Time Series.- 9.5 Stationary Processes.- 9.6 Conclusions and Discussions.- 10. Bayesian Bootstrap and Random Weighting.- 10.1 Bayesian Bootstrap.- 10.2 Random Weighting.- 10.3 Random Weighting for Functional and Linear Models.- 10.4 Empirical Results for Random Weighting.- 10.5 Conclusions and Discussions.- Appendix A. Asymptotic Results.- A.1 Modes of Convergence.- A.2 Convergence of Transformations.- A.4 The Borel-Cantelli Lemma.- A.5 The Law of Large Numbers.- A.6 The Law of the Iterated Logarithm.- A.7 Uniform Integrability.- A.8 The Central Limit Theorem.- A.9 The Berry-Esséen Theorem.- A.10 Edgeworth Expansions.- A.11 Cornish-Fisher Expansions.- Appendix B. Notation.- References.- Author Index.