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

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  • Lei Wang
  • Guangming Deng

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.

Suggested Citation

  • Lei Wang & Guangming Deng, 2017. "Dimension-reduced empirical likelihood inference for response mean with data missing at random," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 594-614, July.
    • Handle: RePEc:taf:gnstxx:v:29:y:2017:i:3:p:594-614
    DOI: 10.1080/10485252.2017.1339307
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