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B0856
Title: Covariate assisted principal regression for covariance matrix outcomes Authors:  Yi Zhao - Indiana University (United States) [presenting]
Abstract: Modeling variances in data has been an important topic in many fields, including in financial and neuroimaging analysis. We consider the problem of regressing covariance matrices on a vector covariates, collected from each observational unit. The main aim is to uncover the variation in the covariance matrices across units that are explained by the covariates. The Covariate Assisted Principal (CAP) regression is introduced, an optimization-based method for identifying the components predicted by (generalized) linear models of the covariates. We develop computationally efficient algorithms to jointly search the projection directions and regression coefficients, and we establish the asymptotic properties. Using extensive simulation studies, the method shows higher accuracy and robustness in coefficient estimation than competing methods. Applied to a resting-state functional magnetic resonance imaging study, the approach identifies the human brain network changes associated with age and sex.