文章基本信息

标题：Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates
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作者：Zhi-Qiang Wang ; Nian-Sheng Tang
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2020
卷号：15
期号：2
页码：579-604
DOI：10.1214/19-BA1165
语种：English
出版社：International Society for Bayesian Analysis
摘要：Bayesian inference on quantile regression (QR) model with mixed discrete and non-ignorable missing covariates is conducted by reformulating QR model as a hierarchical structure model. A probit regression model is adopted to specify missing covariate mechanism. A hybrid algorithm combining the Gibbs sampler and the Metropolis-Hastings algorithm is developed to simultaneously produce Bayesian estimates of unknown parameters and latent variables as well as their corresponding standard errors. Bayesian variable selection method is proposed to recognize significant covariates. A Bayesian local influence procedure is presented to assess the effect of minor perturbations to the data, priors and sampling distributions on posterior quantities of interest. Several simulation studies and an example are presented to illustrate the proposed methodologies.
关键词：Bayesian analysis; local influence analysis; non-ignorable missing data; quantile regression; variable selection