Bayesian Analysis
- Bayesian Anal.
- Volume 11, Number 1 (2016), 1-24.
Bayesian Quantile Regression for Ordinal Models
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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