Weighted empirical likelihood for quantile regression with non ignorable missing covariates

Xiaohui Yuan and Xiaogang Dong

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 12, 3068-3084

Abstract: In this paper, we propose an empirical likelihood-based weighted estimator of regression parameter in quantile regression model with non ignorable missing covariates. The proposed estimator is computationally simple and achieves semiparametric efficiency if the probability of missingness on the fully observed variables is correctly specified. The efficiency gain of the proposed estimator over the complete-case-analysis estimator is quantified theoretically and illustrated via simulation and a real data application.

Date: 2019
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DOI: 10.1080/03610926.2018.1473604

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