JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 279
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Korean International Statistical Society
Abstract #312177
Title: Sparse Robust Graphical Models
Author(s): Myung Hee Lee*+ and Hyonho Chun and James Fleet
Companies: Colorado State University and Purdue University and Purdue University
Keywords: graphical model ; quantile regression ; conditional independence ; robust procedure
Abstract:

A graphical model is a way of inferring conditional relationships among multiple variables. When the variables follow multivariate normal distribution, we can fit Gaussian Graphical Models (GGMs) and identify the conditional independence relationship by the zero entries of the precision matrix. However, when the variables do not follow Gaussian, the conditional independence can no longer be inferred from the precision matrix. We propose a graphical model that is robust to the distributional assumption, and we do this via applying a set of sparse quantile regression models. We show that the conditional quantile probabilities of one variable as function of the rest bear sufficient information on the conditional dependence between variables under appropriate assumption. We demonstrate the advantages of our approach using simulation study under various scenarios and then we apply our method to an interesting real biological dataset, where considerable amount of the dataset is contaminated, illustrating the advantage of the proposed method in a real setting.


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.