IJPAM: Volume 115, No. 1 (2017)

Title

BAYESIAN QUANTILE REGRESSION WITH SCALE
MIXTURE OF UNIFORM PRIOR DISTRIBUTIONS

Authors

Fadel Hamid Hadi Alhusseini
Department of Statistics and Economic Informatics
University of Craiova
ROMANIA

Abstract

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.

History

Received: February 10, 2017
Revised: April 21, 2017
Published: June 29, 2017

AMS Classification, Key Words

AMS Subject Classification: C11, C20, C15
Key Words and Phrases: Bayesian Lasso, MCMC, posterior distributions, quantile regression, scale mixture of normals

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Bibliography

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How to Cite?

DOI: 10.12732/ijpam.v115i1.7 How to cite this paper?

Source:
International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2017
Volume: 115
Issue: 1
Pages: 77 - 91


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CC BY This work is licensed under the Creative Commons Attribution International License (CC BY).