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Activity Number: 285 - Statistical Inference for Probabilistic Graphical Models with Applications
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: ENAR
Abstract #309330
Title: Direct Inference for Sparse Differential Network Analysis
Author(s): Irina Gaynanova* and Byol Kim and Mladen Kolar
Companies: Texas A&M University and University of Chicago and University of Chicago
Keywords: Debiasing; Gaussian graphical model; Multiple Testing; Precision matrix

We consider the problem of constructing confidence intervals for the differential edges between the two high-dimensional networks. The problem is motivated by the comparison of gene interactions between two molecular subtypes of colorectal cancer with distinct survival prognosis. Unlike the existing approaches for differential network inference that require sparsity of individual precision matrices from both groups, we only require sparsity of the precision matrix difference. We discuss the methods' theoretical properties, evaluate its performance in numerical studies and highlight directions for future research. This is joint work with Mladen Kolar and Byol Kim.

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

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