Open Access
March 2016 Bayesian Quantile Regression for Ordinal Models
Mohammad Arshad Rahman
Bayesian Anal. 11(1): 1-24 (March 2016). DOI: 10.1214/15-BA939


The paper introduces a Bayesian estimation method for quantile regression in univariate ordinal models. Two algorithms are presented that utilize the latent variable inferential framework of Albert and Chib (1993) and the normal-exponential mixture representation of the asymmetric Laplace distribution. Estimation utilizes Markov chain Monte Carlo simulation – either Gibbs sampling together with the Metropolis–Hastings algorithm or only Gibbs sampling. The algorithms are employed in two simulation studies and implemented in the analysis of problems in economics (educational attainment) and political economy (public opinion on extending “Bush Tax” cuts). Investigations into model comparison exemplify the practical utility of quantile ordinal models.


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Mohammad Arshad Rahman. "Bayesian Quantile Regression for Ordinal Models." Bayesian Anal. 11 (1) 1 - 24, March 2016.


Published: March 2016
First available in Project Euclid: 4 February 2015

zbMATH: 1357.62126
MathSciNet: MR3447089
Digital Object Identifier: 10.1214/15-BA939

Keywords: asymmetric Laplace , Bush Tax cuts , educational attainment , Gibbs sampling , Markov chain Monte Carlo , Metropolis–Hastings

Rights: Copyright © 2016 International Society for Bayesian Analysis

Vol.11 • No. 1 • March 2016
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