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Activity Number: 19
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #317129 View Presentation
Title: Bayesian Variable Selection for Binary Outcomes in High-Dimensional Settings
Author(s): Wenyi Wang* and Amir Nikooienejad and Valen E. Johnson
Companies: MD Anderson Cancer Center and Texas A&M University and Texas A&M University
Keywords: Non-local priors ; Logistic models ; high-dimensional variable selection
Abstract:

One of the important areas of high dimensional data analysis is variable selection where one might want to find the most contributing variables among the myriad of them. This important problem becomes more difficult when the number of observations is much less than the number of variables. For instance, in bioinformatics and in cancer genomics research where thousands of genes play the role of covariates with a small number of samples. During the past decade, many methods have been proposed such as ISIS and Adaptive LASSO. Recently Johnson and Russell proposed using non-local prior densities on Bayesian model parameters and showed it outperforms the commonly used penalized likelihood methods for continuous outcomes. We utilize these newly proposed non-local priors and introduce a new Bayesian method of variable selection named MOMLogit for the binary outcomes. We have tested this algorithm on simulation data, and demonstrated it performs well under different parameter settings. As our method outperforms the ISIS methods under common conditions, we expect our method to have a significant impact on related applications such as in bioinformatics and computational biology.


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