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Activity Number: 235
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320470
Title: Bayesian Feature Selection for Ultra-High-Dimensional Imaging Genetics Data
Author(s): Yize Zhao* and Hongtu Zhu and Fei Zou
Companies: Statistical and Applied Mathematical Sciences Institute and The University of North Carolina at Chapel Hill and University of Florida
Keywords: Bayesian feature selection ; Imaging Genetics ; Markov chain Monte Carlo ; Sequential sampling ; Ultra high-dimension

This work is motivated by the joint analysis of multivariate phenotypes and ultra-high dimensional genotypes obtained from ADNI. Currently, such joint analysis presents major computational and theoretical challenges for existing statistical methods. The aim of this paper is to propose a novel multilevel sequential selection procedure under a Bayesian multivariate response regression model (MRRM) to select informative features among multivariate responses and ultra high-dimensional predictors. Specifically, we treat the identification of nonzero elements in the sparse coefficient matrix as a hierarchical feature selection problem by first selecting potential nonzero rows among the matrix (genotype selection) and then localizing the nonzero elements within the marked rows (phenotype selection). The genotype-wise selection is accomplished by constructing multilevel auxiliary selection models under different scales with the actual scale auxiliary model treated as another level for the ultimate phenotype-wise selection. This procedure allows the posterior inference be "reweighted'" to concentrate more efficiently on the potential signal

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

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