Symmetric Location Estimation /Testing by Empirical Likelihood

Kyoungmi Kim, Mai Zhou

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

The problem of estimating the center of a symmetric distribution is well studied and many nonparametric procedures are available. It often serves as the test problem for many nonparametric estimation procedures, and stimulated the development of efficient nonparametric estimation theory. We use this familiar setting to illustrate a novel use of empirical likelihood method for estimation and testing. Empirical likelihood is a general nonparametric inference method, see Owen [Owen, A. (2001). Empirical Likelihood. London: Chapman and Hall]. However, for symmetric location problem (and some other problems) empirical likelihood has difficulties. Owen (2001) call them "challenges for the empirical likelihood". We propose and study a way to use the empirical likelihood with such problems by modifying the parameter space. We illustrate this approach by applying it to the symmetric location problem. We show that the usual asymptotic theory of empirical likelihood still holds and the asymptotic efficiency of the so obtained empirical NPMLE of location is studied.

Original languageEnglish (US)
Pages (from-to)2233-2243
Number of pages11
JournalCommunications in Statistics - Theory and Methods
Volume33
Issue number9 SPEC.ISS.
DOIs
Publication statusPublished - Sep 2004
Externally publishedYes

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Keywords

  • Asymptotic chi-square distribution
  • Many constraints of symmetry
  • Nonparametric information bound

ASJC Scopus subject areas

  • Statistics and Probability
  • Safety, Risk, Reliability and Quality

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