Online Program Home
My Program

Abstract Details

Activity Number: 2
Type: Invited
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #318423 View Presentation
Title: Bayesian Feature Screening for Big Neuroimaging Data via Massively Parallel Computing
Author(s): Jian Kang*
Companies: University of Michigan
Keywords: Bayesian variable selection ; high dimensional imaging data ; Autism Disease
Abstract:

Motivated by the needs of selecting important features from big neuroimaging data, we develop a new Bayesian feature screening approach in the generalized linear model (GLM) framework. We assign the conjugate priors on the coefficients and obtain the analytical form of the marginal posterior density function. Under some mild regularity conditions, we show that the marginal posterior moments follow a mixture of normal distributions, one of which component is the standard normal distribution for unimportant variables. In light of this theoretical foundation, we develop a Bayesian variable screening algorithm for ultra-high dimensional data con- sisting of two steps: Step 1: compute a multivariate variable screening statistic based on marginal posterior moments; Step 2: perform the mixture model-based cluster anal- ysis on screening statistics to identify the unimportant variables. Step 1 only requires a computational complexity on the linear order of the number of predictors and it is straightforward to be parallelized. It has a close connection with sure independent screening (SIS) statistics and high-dimensional ordinary least-squares projection (HOLP) methods. Step 2 is an extensi


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

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association