Description:

Biostatistics, Outcomes, & Bioinformatics (BOB)
Seminar Series Announcement
Department of Preventive Medicine
Feinberg School of Medicine
Northwestern University

“Semiparametric odds ratio model for sparse network detection”
Gaussian graphical model for network detection has been extensively studied in high-dimensional settings. This model for network detection relies heavily on the joint normal distribution assumption for its interpretation of the nonzero off-diagonal elements of the precision matrix as the presence of network connection between two nodes. Generalization of the Gaussian graphical model to normal copula model and other generalized linear modeling approaches have been proposed to overcome the problem. However, limitations exist in these generalizations. In this article, we propose a unified graphical modeling approach to network detection through the semiparametric odds ratio model. The semiparametric odds ratio model is flexible in handling both discrete and continuous data and in including interactions, can avoid the problem of model incompatible, and is invariant to biased sampling designs in network detection. A neighborhood selection approach is proposed for the network detection. We show that the approach enjoys sign-consistency under a version of irrepresentable condition. A gradient descent algorithm is proposed to solve the computational problem. The proposed approach is compared with alternative approaches via simulation.

Jinsong Chen, PhD
Research Specialist
Institute for Minority Health Research
University of Illinois - Chicago

Thursday, March 19, 2015
3:00 - 4:00 p.m.

Stamler Conference Room
Suite 1400, 14th Floor
680 North Lake Shore Drive
Chicago, Illinois 60611