JSM 2011 Online Program

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Abstract Details

Activity Number: 411
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #302885
Title: A Bayesian Approach to Multiple Quantiles Estimation in Regression
Author(s): Yunwen Yang*+ and Xuming He
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Address: 725 South Wright Street, Champaign, IL, 61820,
Keywords: Quantile regression ; multiple quantiles ; empirical likelihood ; Bayesian
Abstract:

Quantile regression has developed into a systematic methodology for estimation of conditional quantile functions. Usually, quantile regression estimation is carried out at one percentile level at a time, and the resulting estimates tend to have high variability in the data sparse areas. In this talk, we consider a Bayesian empirical likelihood approach (BEL) to quantile regression, which can naturally incorporate various forms of informative priors to explore the commonality across quantiles. The focus of the presentation is to show how the BEL approach facilitates an efficient way of joint estimation of several quantiles, leading to more efficient quantile estimation, especially in the data sparse areas. We show that the posterior-based inference for BEL is asymptotically valid, and demonstrate both theoretically and empirically how the BEL approach improves efficiency over the usual quantile regression estimators. Finally, we use the BEL approach to quantile regression as a statistical downscaling method in climate studies, and illustrate by example the merit of our proposed BEL approach.


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