Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe).

ABSTRACT: We propose a new method for the simultaneous selection and estimation of multivariate sparse additive models with correlated errors. Our method called Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe) simultaneously selects among null, linear, and smooth non-linear effects for each predictor while incorporating joint estimation of the sparse residual structure among responses, with the motivation that accounting for inter-response correlation structure can lead to improved accuracy in variable selection and estimation efficiency. CoMPAdRe is constructed in a computationally efficient way that allows the selection and estimation of linear and non-linear covariates to be conducted in parallel across responses. Compared to single-response approaches that marginally sel

SUBMITTER: Desai N 

PROVIDER: S-EPMC12333589 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature