This book presents an overview of the bootstrap and related methods for assessing statistical accuracy. Preface, 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 Jacknife, Confidence Intervals Based on Bootstrap ''Tables''. Confidence Intervals Based of 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 Jacknife, An Overview of Nonparametric and Parametric Inference, Further Topics in Bootstrap Confidence Intervals, Efficient Bootstrap Computations, Approximate Likehoods, Bootstrap Bioequivalence, Discussion and Further Topics, Index.