Profile empirical likelihood for parametric and semiparametric models

Lu Lin*, Lixing ZHU, K. C. Yuen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This paper introduces a profile empirical likelihood and a profile conditionally empirical likelihood to estimate the parameter of interest in the presence of nuisance parameters respectively for the parametric and semiparametric models. It is proven that these methods propose some efficient estimators of parameters of interest in the sense of least-favorable efficiency. Particularly, for the decomposable semiparametric models, an explicit representation for the estimator of parameter of interest is derived from the proposed nonparametric method. These new estimations are different from and more efficient than the existing estimations. Some examples and simulation studies are given to illustrate the theoretical results.

Original languageEnglish
Pages (from-to)485-505
Number of pages21
JournalAnnals of the Institute of Statistical Mathematics
Volume57
Issue number3
DOIs
Publication statusPublished - Sep 2005

Scopus Subject Areas

  • Statistics and Probability

User-Defined Keywords

  • Efficiency
  • Empirical likelihood
  • Parametric and semiparametric models
  • Profile likelihood

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