Cover image for The jackknife the bootstrap and other resampling plans
The jackknife the bootstrap and other resampling plans
Personal Author:
CMBS-NSF Regional Conference series in Applied Mathematics
Publication Information:
Philadelphia Pa. : Soc. for Industrial and Applied Mathematics, 1982


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Call Number
Material Type
30000002128829 QA278.8 E47 1982 Open Access Book

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The jackknife and the bootstrap are nonparametric methods for assessing the errors in a statistical estimation problem. They provide several advantages over the traditional parametric approach: the methods are easy to describe and they apply to arbitrarily complicated situations; distribution assumptions, such as normality, are never made.

This monograph connects the jackknife, the bootstrap, and many other related ideas such as cross-validation, random subsampling, and balanced repeated replications into a unified exposition. The theoretical development is at an easy mathematical level and is supplemented by a large number of numerical examples.

The methods described in this monograph form a useful set of tools for the applied statistician. They are particularly useful in problem areas where complicated data structures are common, for example, in censoring, missing data, and highly multivariate situations.

Table of Contents

The Jackknife Estimate of Bias
The Jackknife Estimate of Variance
Bias of the Jackknife Variance Estimate
The Bootstrap
The Infinitesimal Jackknife
The Delta Method and the Influence Function
Cross-Validation, Jackknife and Bootstrap
Balanced Repeated Replications (Half-Sampling)
Random Subsampling
Nonparametric Confidence Intervals