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June 2020 Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates
Zhi-Qiang Wang, Nian-Sheng Tang
Bayesian Anal. 15(2): 579-604 (June 2020). DOI: 10.1214/19-BA1165

Abstract

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.

Citation

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Zhi-Qiang Wang. Nian-Sheng Tang. "Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates." Bayesian Anal. 15 (2) 579 - 604, June 2020. https://doi.org/10.1214/19-BA1165

Information

Published: June 2020
First available in Project Euclid: 19 June 2019

MathSciNet: MR4078726
Digital Object Identifier: 10.1214/19-BA1165

Subjects:
Primary: 62F15, 62H12
Secondary: 62J20

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Vol.15 • No. 2 • June 2020
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