Open Access
February 2009 Empirical likelihood for estimating equations with missing values
Dong Wang, Song Xi Chen
Ann. Statist. 37(1): 490-517 (February 2009). DOI: 10.1214/07-AOS585


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.


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Dong Wang. Song Xi Chen. "Empirical likelihood for estimating equations with missing values." Ann. Statist. 37 (1) 490 - 517, February 2009.


Published: February 2009
First available in Project Euclid: 16 January 2009

zbMATH: 1155.62021
MathSciNet: MR2488360
Digital Object Identifier: 10.1214/07-AOS585

Primary: 62G05
Secondary: 62G20

Keywords: empirical likelihood , estimating equations , Kernel estimation , missing values , nonparametric imputation

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.37 • No. 1 • February 2009
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