Empirical likelihood for estimating equations with missing values

Dong Wang, Song Xi Chen

Research output: Contribution to journalArticle

82 Scopus citations

Abstract

We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wide-ranging, we propose a nonparametric imputation of the missing values from a kernel estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a profile likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can remove the selection bias in the missingness and the empirical likelihood leads to more efficient parameter estimation. The proposed method is further evaluated by simulation and an empirical study on a genetic dataset on recombinant inbred mice.

Original languageEnglish (US)
Pages (from-to)490-517
Number of pages28
JournalAnnals of Statistics
Volume37
Issue number1
DOIs
StatePublished - Feb 2009

Keywords

  • Empirical likelihood
  • Estimating equations
  • Kernel estimation
  • Missing values
  • Nonparametric imputation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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