|
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
|
|
Abstract:
|
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.
