Min-max asymptotic variance of M-estimates of location when scale is unknown

Bing Li, Ruben H. Zamar

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper extends Huber's (1964) min-max result to the case when the scale parameter is unknown and must be estimated along with the location parameter. A min-max problem in which nature chooses F from a family F of symmetric distribution functions around a given location-scale central model, the statistician chooses an M-estimate of location, that is, specifies the influence curve or score function ψ and the auxiliary scale estimate sn, is solved. The optimal choise for sn is an M-estimate of scale applied to the residuals about the median. The optimal choice for the score function ψ is a truncated and rescaled maximum likelihood score function for the central model. In he Gaussian case rescaling is not necessary and so, except for the truncation point which is now smaller, Huber's (1964) classical result obtains.

Original languageEnglish (US)
Pages (from-to)139-145
Number of pages7
JournalStatistics and Probability Letters
Volume11
Issue number2
DOIs
StatePublished - Jan 1 1991

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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