Bayesian empirical likelihood of quantile regression with missing observations.

In this paper, we focus on partially linear varying coefficient quantile regression with observations missing at random, which allows the responses or responses and covariates simultaneously missing. By means of empirical likelihood method, we construct posterior distributions of the parameter in the model, and investigate their large sample properties under fixed prior. Meanwhile, we use a Bayesian hierarchical model based on empirical likelihood, spike and slab Gaussian priors to discuss variable selection. By using MCMC algorithm, finite sample performance of the proposed methods is investigated via simulations, and real data analysis is discussed too. [ABSTRACT FROM AUTHOR]