Standard Article

Jackknife Resampling

Statistical and Numerical Computing

  1. Herwig Friedl1,
  2. Erwin Stampfer2

Published Online: 15 SEP 2006

DOI: 10.1002/9780470057339.vaj001

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Friedl, H. and Stampfer, E. 2006. Jackknife Resampling. Encyclopedia of Environmetrics. 2.

Author Information

  1. 1

    Technical University Graz, Austria

  2. 2

    Technical University, Graz, Austria

Publication History

  1. Published Online: 15 SEP 2006

Abstract

The jackknife is a particular resampling method that aims primarily at the calculation of the bias and the variance of estimates, without making very restrictive distributional assumptions. It is nonparametric and easy to apply in quite general settings. As with other resampling techniques, the jackknife can be computationally very intensive. In the simplest case jackknife resampling is generated by sequentially deleting single cases from the original sample (delete-one jackknife). A more generalized jackknife technique uses resampling that is based on multiple case deletion (delete-d jackknife). The main part of this article focuses on independently and (in the introductory part also) identically sampled data. It then discusses the basics of the jackknife and its applications to nonidentically distributed observations that can be described by linear, generalized linear, nonlinear, and Cox's regression models. Finally, the article gives an account of jackknife estimates for dependent data, e.g. in the context of generalized estimating equations or special time series models.