A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions.

ABSTRACT: The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradigm. The selected sparsity patterns are crucial to help understand the network of relationships between the predictor and response variables, as well as the conditional relationships among the latter. While Bayesian methods have the advantage of providing natural uncertainty quantification through posterior inclusion probabilities and credible intervals, current Bayesian approaches either restrict to specific sub-classes of sparsity patterns and/or are not scalable to settings with hundreds of responses and predictors. Bayesian approaches which only focus on estimating the posterior mod

SUBMITTER: Samanta S 

PROVIDER: S-EPMC9881595 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature