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Bayesian model selection in ordinal quantile regression

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  • Alhamzawi, Rahim

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.

Suggested Citation

  • Alhamzawi, Rahim, 2016. "Bayesian model selection in ordinal quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 68-78.
    • Handle: RePEc:eee:csdana:v:103:y:2016:i:c:p:68-78
    DOI: 10.1016/j.csda.2016.04.014
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    5. Fadel Hamid Hadi Alhusseini & Taha al Shaybawee & Fedaa Abd Almajid Sabbar Alaraje, 2017. "Identify Relative importance of covariates in Bayesian lasso quantile regression via new algorithm in statistical program R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 99-110, December.

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