Bayesian Composite Quantile Regression

• Activity Number: 365
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): Hanwen Huang* and Zhongxue Chen
• Companies: University of Georgia and Indiana University - Bloomington
• Keywords: Laplace Prior ; Mixture Model ; Quantile Regression ; Variable Selection

Abstract

One advantage of quantile regression, relative to the ordinary least squares (OLS) regression, is that the quantile regression estimates are more robust against outliers and non-normal errors in the response measurements. However, the relative efficiency of the quantile regression estimator with respect to the OLS estimator can be arbitrarily small. To overcome this problem, composite quantile regression methods have been proposed in the literature which are resistant to heavy-tailed errors or outliers in the response and at the same time are more efficient than the traditional single quantile based quantile regression method. This paper studies the composite quantile regression from a Bayesian perspective. The advantage of the Bayesian hierarchical framework is that the weight of each component in the composite model can be treated as open parameter and automatically estimated through Markov Chain Monte Carlo sampling procedure. Moreover, the lasso regularization can be naturally incorporated into the model to perform variable selection. The performance of the proposed method over the single quantile based method was demonstrated via extensive simulations and real data analysis.