Transfer learning in high-dimensional semiparametric graphical models with application to brain connectivity analysis.

ABSTRACT: Transfer learning has drawn growing attention with the target of improving statistical efficiency of one study (dataset) by digging up information from similar and related auxiliary studies (datasets). In this article, we consider transfer learning problem in estimating undirected semiparametric graphical model. We propose an algorithm called Trans-Copula-CLIME for estimating an undirected graphical model while uncovering information from similar auxiliary studies, characterizing the similarity between the target graph and each auxiliary graph by the sparsity of a divergence matrix. The proposed method relaxes the restrictive Gaussian distribution assumption, which deviates from reality for the fMRI dataset related to attention deficit hyperactivity disorder (ADHD) considered here. Nonpara

SUBMITTER: He Y 

PROVIDER: S-EPMC9497459 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature