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The Jackknife, the Bootstrap, and Other Resampling Plans

Part of CBMS-NSF Regional Conference Series in Applied Mathematics

Author: Bradley Efron

Date Published: January 1982
availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
format: Paperback
isbn: 9780898711790

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Description

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.

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Product details
- Date Published: January 1982
- format: Paperback
- isbn: 9780898711790
- length: 100 pages
- dimensions: 252 x 172 x 8 mm
- weight: 0.184kg
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
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.
Author
Bradley Efron