ArticleBiostatistics (Oxford, England)2025
Bayesian estimation of covariate assisted principal regression for brain functional connectivity.
Article in Biostatistics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Fast Bayesian Functional Principal Components Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- Sparse covariate-driven factorization of high-dimensional brain connectivity with application to site effect correction.Biostatistics (Oxford, England) · 2026Article
- Sparse covariate-driven factorization of high-dimensional brain connectivity with application to site effect correction.Biostatistics (Oxford, England) · 2026Article
- Bayesian scalar-on-network regression with applications to brain functional connectivity.Biometrics · 2025Article
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Authors and funding
1 author.
Funding
Abstract
This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach to the covariance matrices and leveraging Euclidean geometry, we estimate dimension reduction parameters and model covariance heterogeneity based on covariates. This method enables joint estimation and uncertainty quantification of relevant model parameters associated with heteroscedasticity. We demonstrate our approach through simulation studies and apply it to analyze associations between covariates and brain functional connectivity using data from the Human Connectome Project.
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Registered trials
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