Journal
ANNALS OF STATISTICS
Volume 37, Issue 1, Pages 490-517Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/07-AOS585
Keywords
Empirical likelihood; estimating equations; Kernel estimation; missing values; nonparametric imputation
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Funding
- NSF [SES-05-18904, DMS-06-04563]
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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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