Online Program Home
My Program

Abstract Details

Activity Number: 23 - Recents Advances in Statistical Learning and Network Data Analysis
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329616 Presentation
Title: High-Dimensional Gaussian Graphical Model for Network-Linked Data
Author(s): Ji Zhu* and Boang Liu and Tianxi Li and Cheng Qian and Elizaveta Levina
Companies: University of Michigan and University of Michigan and University of Michigan and University of Michigan and University of Michigan
Keywords: Covariance estimation; Graphical models; High-dimensional data; Network analysis; Node covariates; Sparse estimation

Graphical models are commonly used in representing conditional independence between random variables, and learning the conditionalindependence structure from data has attracted much attention inrecent years. However, almost all commonly used graph learning methods rely on the assumption that the observations share the same mean vector. In this paper, we extend the Gaussian graphical model to the setting where the observations are connected by a network and propose a model that allows the mean vectors for different observations to be different. We have developed an efficient estimation method for the model and demonstrated the effectiveness of the proposed method using simulation studies. Further, we prove that under the assumption of "network cohesion", the proposed method can estimate both the inverse covariance matrix and the corresponding graph structure accurately. We have also applied the proposed method to a dataset consisting of statisticians' coauthorship network to learn the statistical term dependency based on the authors' publications and obtained meaningful results.

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

Back to the full JSM 2018 program