Jackknifing r-estimators

William R. Schucany, Simon J. Sheather

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Sufficient conditions are given for the consistency of the jackknife variance estimator for R-estimators of location in the one- and two-sample problems. In particular, the jackknife is shown to produce strongly consistent estimates of the variance of the Hodges-Lehmann estimator. An efficient algorithm for computing the variance estimator is presented. For the two sample sizes considered in this paper, calculations for the jackknife estimator of the variance of the Hodges-Lehmann estimator are about 50 times as fast as the bootstrap variance estimator with B = 100. Some Monte Carlo evidence of the small-sample efficiency of this jackknife variance estimator is reported. Extensions of the results to R-estimators in the linear model context are discussed.

Original languageEnglish
Pages (from-to)393-398
Number of pages6
Issue number2
StatePublished - Jun 1989

Bibliographical note

Funding Information:
The research of W. R. Schucany was partially supported by a contract from the U.S. Office of Naval Research. The research was completed while he was visiting the Institute of Advanced Studies, Australian National University.


  • Differentiability
  • Hodges-Lehmann
  • Rank estimator
  • Standard error

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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