A random covariance model for bi-level graphical modeling with application to resting-state fMRI data.

ABSTRACT: We consider a novel problem, bi-level graphical modeling, in which multiple individual graphical models can be considered as variants of a common group-level graphical model and inference of both the group- and individual-level graphical models is of interest. Such a problem arises from many applications, including multi-subject neuro-imaging and genomics data analysis. We propose a novel and efficient statistical method, the random covariance model, to learn the group- and individual-level graphical models simultaneously. The proposed method can be nicely interpreted as a random covariance model that mimics the random effects model for mean structures in linear regression. It accounts for similarity between individual graphical models, identifies group-level connections that are shared by

SUBMITTER: Zhang L 

PROVIDER: S-EPMC7914259 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature