Weighted empirical likelihood for quantile regression with nonignorable missing covariates
Methodology
2017-10-10 v2
Abstract
In this paper, we propose an empirical likelihood-based weighted estimator of regression parameter in quantile regression model with nonignorable 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.
Cite
@article{arxiv.1703.01866,
title = {Weighted empirical likelihood for quantile regression with nonignorable missing covariates},
author = {Xiaohui Yuan and Xiaogang Dong},
journal= {arXiv preprint arXiv:1703.01866},
year = {2017}
}
Comments
20 pages,2 figures