An Introduction to the BootstrapStatistics 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. |
Contents
The bootstrap estimate of standard error | 6 |
The empirical distribution function and the plugin | 31 |
Standard errors and estimated standard errors | 39 |
More complicated data structures | 86 |
Regression models | 105 |
7 | 116 |
Estimates of bias | 126 |
The jackknife | 141 |
Crossvalidation and other estimates of prediction | 237 |
Adaptive estimation and calibration | 258 |
Assessing the error in bootstrap estimates | 271 |
A geometrical representation for the bootstrap | 283 |
An overview of nonparametric and parametric | 296 |
Further topics in bootstrap confidence intervals | 321 |
Efficient bootstrap computations | 338 |
Approximate likelihoods | 358 |
Confidence intervals based on bootstrap tables | 153 |
Confidence intervals based on bootstrap | 168 |
Better bootstrap confidence intervals | 178 |
42 | 199 |
Permutation tests | 202 |
Hypothesis testing with the bootstrap | 220 |
Bootstrap bioequivalence | 372 |
Discussion and further topics | 392 |
software for bootstrap computations | 398 |
References | 413 |
Author index | 426 |
Other editions - View all
Common terms and phrases
algorithm approximation biasjack boot bootstrap computations bootstrap confidence intervals bootstrap data set bootstrap methods bootstrap samples bootstrap standard error bootstrap-t interval calculate Chapter components compute confidence intervals confidence point covariance cross-validation curve data points defined delta method density discussed distribution F Efron empirical distribution empirical distribution function endpoints equal estimate bias estimate of bias estimate of standard estimated standard error example Fisher information floess formula gives histogram hormone data jackknife estimate least-squares left panel linear LSAT matrix median mouse data nonparametric normal distribution normal theory number of bootstrap observed obtained panel of Figure parameter parametric bootstrap percentile interval permutation test plug-in estimate plug-in principle population prediction error probability distribution problem quadratic random sample random variable resampling right panel shows standard deviation standard error standard error estimate standard normal strap Suppose Table theta tion transformation vector