Dimension-reduced empirical likelihood inference for response mean with data missing at random

Lei Wang and Guangming Deng

Journal of Nonparametric Statistics, 2017, vol. 29, issue 3, 594-614

Abstract: To make efficient inference for mean of a response variable when the data are missing at random and the dimension of covariate is not low, we construct three bias-corrected empirical likelihood (EL) methods in conjunction with dimension-reduced kernel estimation of propensity or/and conditional mean response function. Consistency and asymptotic normality of the maximum dimension-reduced EL estimators are established. We further study the asymptotic properties of the resulting dimension-reduced EL ratio functions and the corresponding EL confidence intervals for the response mean are constructed. The finite-sample performance of the proposed estimators is studied through simulation, and an application to HIV-CD4 data set is also presented.

Date: 2017
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DOI: 10.1080/10485252.2017.1339307

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