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

Abstract Details

Activity Number: 147
Type: Invited
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305954
Title: A Stochastic Partitioning Method to Associate High-Dimensional Data Sets
Author(s): Mahlet G. Tadesse*+ and Stefano Monni
Companies: Georgetown University and Weill Cornell Medical College
Address: Department of Mathematics, Washington, DC, 20057,
Keywords: multivariate model selection ; mixture models ; Markov chain Monte Carlo ; parallel tempering ; integration of genomic datasets
Abstract:

Several procedures have been developed to associate high-dimensional covariate data to univariate outcomes. In recent years, there has been a growing interest in relating data sets in which both the number of regressors and response variables are substantially larger than the sample size. For example, in an attempt to gain new insights into molecular processes, many efforts are being carried out to integrate data from various high-throughput genomic experiments. We propose a Bayesian stochastic partitioning method to identify sets of covariates associated with correlated outcomes. The procedure provides a unified framework for uncovering response variables with similar expression profiles and determining subsets of predictors that modulate these patterns. We illustrate the method with applications to genomic data sets.


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