JSM 2012 Home

JSM 2012 Online Program

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

Online Program Home

Abstract Details

Activity Number: 465
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306020
Title: Sparse Covariate Dependent Binary Markov Networks
Author(s): Jie Cheng*+ and Ji Zhu and Liza Levina
Companies: University of Michigan and University of Michigan and University of Michigan
Address: Department of Statistics, Ann Arbor, MI, 48109, United States
Keywords: binary markov network ; sparse ; conditional likelihood ; regularized logistic regression ; gene network

Motivated by the availability of informative covariates or features when modeling the network structure for multivariate binary data, we propose a sparse covariate dependent Ising model to study the conditional dependency patterns of the binary variables and their relationship with the covariates. Our model relaxes the i.i.d. assumption of the network data commonly used in the literature and naturally incorporates the covariate information to produce subject-specific graphical models. Assuming both the underlying graphs and the effective covariates for each edge being sparse, we propose two approaches with $l_1$ regularization to fit the model. One is based on neighborhood selection and the other on pseudo likelihood. Asymptotic results are established for the estimates. We design several sets of simulation studies to illustrate the patterns of selection performance using the proposed methods. We conclude with an application of our model on a genomic instability data from tumor samples.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

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

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