Online Program Home
My Program

Abstract Details

Activity Number: 256 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #330580
Title: Bayesian Functional Quantile Regression
Author(s): Yusha Liu* and Jeffrey S Morris and Meng Li
Companies: Rice University and The University of Texas M.D. Anderson Cancer Center and Rice University
Keywords: Bayesian modeling; quantile regression; shrinkage priors

We propose a unified Bayesian functional quantile regression framework which simultaneously performs quantile regression and adaptively regularizes the functional coefficients by projection into a basis space and employing an appropriate shrinkage prior on the basis coefficients. Our framework is highly general in that any basis functions and shrinkage priors can be chosen, depending on the characteristics of the functional data to be analyzed. We developed an efficient Gibbs sampler to fit this fully Bayesian hierarchical model automatically with no tuning required, which yield posterior samples that can be used to perform pointwise or joint inference on any model quantity of interest. Our approach is computationally efficient and can handle functional data observed on grids of hundreds to thousands.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program