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Activity Number: 248
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319435 View Presentation
Title: Adaptively Choosing Hyperparameters for Non-Local Priors in High Dimensional Bayesian Variable Selection
Author(s): Amir Nikooienejad* and Valen E. Johnson
Companies: Texas A&M University and Texas A&M University
Keywords: Bayesian Variable Selection ; Non-Local Prior ; Cancer Genomics ; High Dimensional Data Analysis

The advent of new genomic technologies has resulted in the production of massive data sets. Analyses of these data require new statistical and computational methods. In this article, we propose one such method that is useful in selecting explanatory variables for prediction of a binary response. we adopt a Bayesian approach that utilizes a mixture of non-local prior densities and point masses on the binary regression coefficient vectors. The resulting method, which we call iMOMLogit, provides improved performance in identifying true models and reducing estimation and prediction error in a number of simulation studies. We also describe a novel approach for setting prior hyperparameters by examining the total variation distance between the prior distributions on the regression parameters and the distribution of the maximum likelihood estimator under the null distribution. Finally, we describe a computational algorithm that can be used to implement iMOMLogit in ultrahigh-dimensional settings (p _ n) and provide diagnostics to assess the probability that this algorithm has identified the highest posterior probability model.

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

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