Statistical inference on high-dimensional covariate-dependent Gaussian graphical regressions.

ABSTRACT: In many genomic studies, gene co-expression graphs are influenced by subject-level covariates like single nucleotide polymorphisms. Traditional Gaussian graphical models ignore these covariates and estimate only population-level networks, potentially masking important heterogeneity. Covariate-dependent Gaussian graphical regressions address this limitation by regressing the precision matrix on covariates, thereby modeling how graph structures vary with high-dimensional subject-specific covariates. To fit the model, we adopt a multi-task learning approach that achieves lower error rates than node-wise regressions. Yet, the important problem of statistical inference in this setting remains largely unexplored. We propose a class of debiased estimators based on multi-task learners, which can b

SUBMITTER: Meng X 

PROVIDER: S-EPMC12720500 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature