BAYESIAN QUANTILE REGRESSION WITH SCALE
MIXTURE OF UNIFORM PRIOR DISTRIBUTIONS
Fadel Hamid Hadi Alhusseini
Department of Statistics and Economic Informatics
University of Craiova
In this paper, we propose a new Bayesian Lasso quantile regression method for variable selection assigning independent scale-mixture of uniform distributions for the regression coefficients.
Then, a simple and efficient MCMC algorithm was presented for Bayesian sampler. Simulation studies and a real data set are
used to investigate the performance of the proposed method compared to some other existing methods. Both simulated and real data examples show that the proposed method performs quite good compared to the other methods under a variety of scenarios.
Received: February 10, 2017
Revised: April 21, 2017
Published: June 29, 2017
AMS Subject Classification: C11, C20, C15
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Key Words and Phrases: Bayesian Lasso, MCMC, posterior distributions, quantile regression, scale mixture of normals
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DOI: 10.12732/ijpam.v115i1.7 How to cite this paper?
International Journal of Pure and Applied Mathematics
ISSN printed version:
ISSN on-line version:
77 - 91
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This work is licensed under the Creative Commons Attribution International License (CC BY).