This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 493
Type: Invited
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract - #306202
Title: Bayesian Partition Models for Detecting Interactions
Author(s): Jun Liu*+
Companies: Harvard University
Address: 605 South Ave, Weston, MA, 02493, United States
Keywords: Genetics ; case control ; quantitative trait loci mapping ; multivariate regression ; variable selection ; model selection

Suppose we have N individuals and for each individual we observed its response vector variable (Yi1,., Yiq) and its p-dimensional categorical-valued covariates (Xi1,., Xip). Our goal is to discover which subset of the response variables is influenced by which subset of the covariates. Although the problem is similar to the multiple-response regression, our goal is much more ambitious than just finding certain linear relationships. I will present a novel Bayesian partition model through the use of a set of latent indicator vectors, together with a Markov chain Monte Carlo algorithm, to tackle the problem. I will illustrate the power of the method mainly using examples in genome-wide genetic association studies and in studies of expression quantitative trait loci (eQTL).

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