Includes bibliographical references (pages 413-425) and indexes.

Introduction -- The accuracy of a sample mean -- Random samples and probabilities -- The empirical distribution function and the plug-in principle -- Standard errors and estimated standard errors -- The bootstrap estimate of standard error -- Bootstrap standard errors: some examples -- More complicated data structures -- Regression models -- Estimates of bias -- The jackknife -- Confidence intervals based on bootstrap "tables" -- Confidence intervals based on bootstrap percentiles -- Better bootstrap confidence intervals -- Permutation tests -- Hypothesis testing with the bootstrap -- Cross-validation and other estimates of prediction error -- Adaptive estimation and calibration -- Assessing the error in bootstrap estimates -- A geometrical representation for the bootstrap and jackknife -- An overview of nonparametric and parametric inference -- Further topics in bootstrap confidence intervals -- Efficient bootstrap computations -- Approximate likelihoods -- Bootstrap bioequivalence -- Discussion and further topics -- Appendix: software for bootstrap computations. An exploration of the many different bootstrap techniques. It discusses useful statistical techniques through real data examples and covers nonparametric regression, density estimation, classification trees, and least median squares regression. There are numerous exercises.