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September 2016 Bayesian Quantile Regression Based on the Empirical Likelihood with Spike and Slab Priors
Ruibin Xi, Yunxiao Li, Yiming Hu
Bayesian Anal. 11(3): 821-855 (September 2016). DOI: 10.1214/15-BA975

Abstract

In this paper, we consider nonparametric Bayesian variable selection in quantile regression. The Bayesian model is based on the empirical likelihood, and the prior is chosen as the “spike-and-slab” prior–a mixture of a point mass at zero and a normal distribution. We show that the posterior distribution of the zero coefficients converges to a point mass at zero and that of the nonzero coefficients converges to a normal distribution. To further address the problem of low statistical efficiency in extreme quantile regression, we extend the Bayesian model such that it can integrate information at multiple quantiles to provide more accurate inference of extreme quantiles for homogenous error models. Simulation studies demonstrate that the proposed methods outperform or perform equally well compared with existing methods. We apply this Bayesian method to study the role of microRNAs on regulating gene expression and find that the regulation of microRNA may have a positive effect on the gene expression variation.

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Ruibin Xi. Yunxiao Li. Yiming Hu. "Bayesian Quantile Regression Based on the Empirical Likelihood with Spike and Slab Priors." Bayesian Anal. 11 (3) 821 - 855, September 2016. https://doi.org/10.1214/15-BA975

Information

Published: September 2016
First available in Project Euclid: 9 October 2015

zbMATH: 1357.62181
MathSciNet: MR3543910
Digital Object Identifier: 10.1214/15-BA975

Rights: Copyright © 2016 International Society for Bayesian Analysis

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Vol.11 • No. 3 • September 2016
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