Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies.

ABSTRACT: Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor. We develop an efficient alternating gradient descent algorithm for parameter estimation, and establish the non-asymptotic error bound for the actual estimator from the algorithm, which quantifies the interplay between the co

SUBMITTER: Zhang J 

PROVIDER: S-EPMC10035565 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature