An Introduction to the Bootstrap

Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.

IntroductionThe Accuracy of a Sample MeanRandom Samples and ProbabilitiesThe Empirical Distribution Function and the Plug-In PrincipleStandard Errors and Estimated Standard ErrorsThe Bootstrap Estimate of Standard ErrorBootstrap Standard Errors: Some ExamplesMore Complicated Data StructuresRegression ModelsEstimates of BiasThe JackknifeConfidence Intervals Based on Bootstrap "Tables"Confidence Intervals Based on Bootstrap PercentilesBetter Bootstrap Confidence IntervalsPermutation TestsHypothesis Testing with the BootstrapCross-Validation and Other Estimates of Prediction ErrorAdaptive Estimation and CalibrationAssessing the Error in Bootstrap EstimatesA Geometrical Representation for the Bootstrap and JackknifeAn Overview of Nonparametric and Parametric InferenceFurther Topics in Bootstrap Confidence IntervalsEfficient Bootstrap ComputationsApproximate LikelihoodsBootstrap BioequivalenceDiscussion and Further TopicsAppendix: Software for Bootstrap ComputationsReferences