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Bayesian model selection in ordinal quantile regression

Rahim Alhamzawi ()

Computational Statistics & Data Analysis, 2016, vol. 103, issue C, 68-78

Abstract: A Bayesian stochastic search variable selection (BSSVS) method is presented for variable selection in quantile regression (QReg) for ordinal models. A Markov Chain Monte Carlo (MCMC) method is adopted to draw the unknown quantities from the full posteriors. Through simulations and analysis of an educational attainment dataset, the performance of the proposed approach is compared with some existing approaches, showing that the proposed approach performs quite good in comparison to some other methods.

Keywords: Bayesian inference; MCMC; Quantile regression; Ordinal models; SSVS (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.csda.2016.04.014

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Handle: RePEc:eee:csdana:v:103:y:2016:i:c:p:68-78