ArticleBiostatistics (Oxford, England)2021
Covariate Assisted Principal regression for covariance matrix outcomes.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Sparse covariate-driven factorization of high-dimensional brain connectivity with application to site effect correction.Biostatistics (Oxford, England) · 2026Article
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Mediation analysis with graph mediator.Biostatistics (Oxford, England) · 2025Article
- Covariance-on-covariance regression.Biometrics · 2025Article
- Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds.Journal of machine learning research : JMLR · 2025Article
- Asymmetric canonical correlation analysis of Riemannian and high-dimensional data.Electronic journal of statistics · 2025Article
- Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury.Human brain mapping · 2024Article
- Longitudinal regression of covariance matrix outcomes.Biostatistics (Oxford, England) · 2024Article
- Identifying covariate-related subnetworks for whole-brain connectome analysis.Biostatistics (Oxford, England) · 2024Article
- Covariance regression with random forests.BMC bioinformatics · 2023Article
- Genetic underpinnings of brain structural connectome for young adults.Journal of the American Statistical Association · 2023Article
- Regression models for partially localized fMRI connectivity analyses.Frontiers in neuroimaging · 2023Article
- Harmonizing functional connectivity reduces scanner effects in community detection.NeuroImage · 2022Article
- Mitigating site effects in covariance for machine learning in neuroimaging data.Human brain mapping · 2022Article
- Beyond massive univariate tests: Covariance regression reveals complex patterns of functional connectivity related to attention-deficit/hyperactivity disorder, age, sex, and response control.Biological psychiatry global open science · 2022Article
- Global Brain Functional Network Connectivity in Infants With Prenatal Opioid Exposure.Frontiers in pediatrics · 2022Article
- Multimodal neuroimaging data integration and pathway analysis.Biometrics · 2021Article
- Principal regression for high dimensional covariance matrices.Electronic journal of statistics · 2021Article
- A whole-brain modeling approach to identify individual and group variations in functional connectivity.Brain and behavior · 2021Article
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5 authors.
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Abstract
In this study, we consider the problem of regressing covariance matrices on associated covariates. Our goal is to use covariates to explain variation in covariance matrices across units. As such, we introduce Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model approach. We develop computationally efficient algorithms to jointly search for common linear projections of the covariance matrices, as well as the regression coefficients. Under the assumption that all the covariance matrices share identical eigencomponents, we establish the asymptotic properties. In simulation studies, our CAP method shows higher accuracy and robustness in coefficient estimation over competing methods. In an example resting-state functional magnetic resonance imaging study of healthy adults, CAP identifies human brain network changes associated with subject demographics.
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