JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 25
Type: Topic Contributed
Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #311954 View Presentation
Title: Analysis of Big Imaging Data via a Bayesian Thresholding Approach
Author(s): Jian Kang*+
Companies: Emory University
Keywords: Big Data ; NeuroImaging ; MCMC ; Truncated Normal Mixture
Abstract:

Motivated by the needs of performing feature selections from big imaging data, we propose a new class of thresholding priors to induce sparsity in the Bayesian paradigm. The thresholding priors can be equivalently represented through three-component mixture priors having a probability mass at zero, but it avoids the daunting posterior computations in high dimensions that are encountered by the usual two-component mixture priors. We demonstrate that the thresholding priors enjoy the advantages of both local priors and non-local priors. To make efficient posterior inference, we develop a Markov chain Monte Carlo (MCMC) algorithm and a fast approximate Bayesian computation (ABC) algorithm which can be scaled up for ultra-high dimensional data analysis. We illustrate our method and compare it with existing methods through simulation studies and a real imaging data application.


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

Back to the full JSM 2014 program




2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.