- Bayesian Anal.
- Advance publication (2018), 26 pages.
Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates
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.
Bayesian Anal., Advance publication (2018), 26 pages.
First available in Project Euclid: 19 June 2019
Permanent link to this document
Digital Object Identifier
Wang, Zhi-Qiang; Tang, Nian-Sheng. Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates. Bayesian Anal., advance publication, 19 June 2019. doi:10.1214/19-BA1165. https://projecteuclid.org/euclid.ba/1560909811
- Supplementary Material of “Bayesian Quantile regression with mixed discrete and nonignorable missing covariates”.