Bayesian Quantile Regression for Ordinal Models

By M. A. Rahman. 2016

47 citations recorded by the source corpus.

. 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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9060d0ee8a41936b7d32969e0fd2b64559a668cc

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Citations in the corpus

Bayesian quantile regression for ordinal longitudinal data (2016, methodology): “longitudinal data, referred to as ‘BQOL’, with Bayesian Quantile Regression for Ordinal Models (BQROR) reported byRahman [39].”