Weighted empirical likelihood for quantile regression with non ignorable missing covariates |
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Authors: | Xiaohui Yuan Xiaogang Dong |
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Affiliation: | School of Basic Science, Changchun University of Technology, Changchun, Jilin, China |
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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. |
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Keywords: | Complete-case-analysis estimator empirical likelihood non ignorable missing covariates quantile regression |
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