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Bayesian quantile regression for ordinal models. (English) Zbl 1357.62126

Summary: 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 J. H. Albert and S. Chib [J. Am. Stat. Assoc. 88, No. 422, 669–679 (1993; Zbl 0774.62031)] 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.

MSC:

62F15 Bayesian inference
62F07 Statistical ranking and selection procedures
62P20 Applications of statistics to economics

Citations:

Zbl 0774.62031
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Full Text: DOI arXiv Euclid

References:

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