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Statistics and Computing

, Volume 9, Issue 1, pp 3–8 | Cite as

Delete-m Jackknife for Unequal m

  • Frank M. T. A. Busing
  • Erik Meijer
  • Rien Van Der Leeden
Article

Abstract

In this paper, the delete-mj jackknife estimator is proposed. This estimator is based on samples obtained from the original sample by successively removing mutually exclusive groups of unequal size. In a Monte Carlo simulation study, a hierarchical linear model was used to evaluate the role of nonnormal residuals and sample size on bias and efficiency of this estimator. It is shown that bias is reduced in exchange for a minor reduction in efficiency. The accompanying jackknife variance estimator even improves on both bias and efficiency, and, moreover, this estimator is mean-squared-error consistent, whereas the maximum likelihood equivalents are not.

jackknife Monte Carlo simulation hierarchical linear model (un)balanced samples maximum likelihood estimation 

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Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Frank M. T. A. Busing
  • Erik Meijer
  • Rien Van Der Leeden

There are no affiliations available

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