## 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. |

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### Contents

The accuracy of a sample mean | 10 |

Random samples and probabilities | 17 |

The empirical distribution function and the plugin | 31 |

Standard errors and estimated standard errors | 39 |

The bootstrap estimate of standard error | 45 |

some examples | 60 |

More complicated data structures | 86 |

Regression models | 105 |

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 |

Estimates of bias | 124 |

The jackknife | 141 |

Confidence intervals based on bootstrap tables | 153 |

Confidence intervals based on bootstrap | 168 |

Better bootstrap confidence intervals | 178 |

Permutation tests | 202 |

Hypothesis testing with the bootstrap | 220 |

Bootstrap bioequivalence | 372 |

Discussion and further topics | 392 |

software for bootstrap computations | 398 |

413 | |

426 | |

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### Common terms and phrases

accuracy accurate analysis applied approach approximation assume average bias boot bootstrap estimate bootstrap replications bootstrap samples bootstrap-t calculations called Chapter close coefficient components compute confidence intervals correct correlation curve data points data set defined Denote density derived described discussed distribution empirical distribution equal estimate of standard example expectation Figure formula function given gives histogram hormone hypothesis important indicate inference interest jackknife least left panel less likelihood limits linear matrix mean measures median method nonparametric normal notes Notice observed obtained original parameter percentile permutation plug-in points population probability problem procedure quantity random sample reasonable regression require sample mean shown shows simple situation squared standard error statistic strap Suppose Table theory tion transformation true usually variable variance vector