Bayesian empirical likelihood of quantile regression with missing observations

Chang-Sheng Liu and Han-Ying Liang ()
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Chang-Sheng Liu: Henan University of Urban Construction
Han-Ying Liang: Tongji University

Metrika: International Journal for Theoretical and Applied Statistics, 2023, vol. 86, issue 3, No 2, 285-313

Abstract: Abstract 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.

Keywords: Bayesian empirical likelihood; Missing at random; Posterior distribution; Quantile regression; Variable selection; 62C10; 62E20 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00184-022-00869-y

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