Bayesian Analysis

Bayesian Quantile Regression for Ordinal Models

Mohammad Arshad Rahman

Full-text: Open access

Abstract

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.

Article information

Source
Bayesian Anal., Volume 11, Number 1 (2016), 1-24.

Dates
First available in Project Euclid: 4 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.ba/1423083637

Digital Object Identifier
doi:10.1214/15-BA939

Mathematical Reviews number (MathSciNet)
MR3447089

Zentralblatt MATH identifier
1357.62126

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

Citation

Rahman, Mohammad Arshad. Bayesian Quantile Regression for Ordinal Models. Bayesian Anal. 11 (2016), no. 1, 1--24. doi:10.1214/15-BA939. https://projecteuclid.org/euclid.ba/1423083637


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