scholarly journals Covariate Assisted Principal Regression for Covariance Matrix Outcomes

2018 ◽  
Author(s):  
Yi Zhao ◽  
Bingkai Wang ◽  
Stewart H. Mostofsky ◽  
Brian S. Caffo ◽  
Xi Luo
Keyword(s):  
Linear Models ◽  
Asymptotic Properties ◽  
Imaging Study ◽  
Brain Network ◽  
Covariance Matrices ◽  
Simulation Studies ◽  
Functional Magnetic Resonance ◽  
Computationally Efficient ◽  
Coefficient Estimation ◽  
Computationally Efficient Algorithms

AbstractModeling 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. This paper introduces Covariate Assisted Principal (CAP) regression, 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, our method shows higher accuracy and robustness in coefficient estimation than competing methods. Applied to a resting-state functional magnetic resonance imaging study, our approach identifies the human brain network changes associated with age and sex.

scholarly journals Covariate Assisted Principal regression for covariance matrix outcomes

Biostatistics ◽  
2019 ◽  
Author(s):  
Yi Zhao ◽  
Bingkai Wang ◽  
Stewart H Mostofsky ◽  
Brian S Caffo ◽  
Xi Luo
Keyword(s):  
Asymptotic Properties ◽  
Imaging Study ◽  
Brain Network ◽  
Covariance Matrices ◽  
Simulation Studies ◽  
Computationally Efficient ◽  
Coefficient Estimation ◽  
Computationally Efficient Algorithms ◽  
Cap Method ◽  
Model Approach

Summary 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.

Prediction of Finite Population Proportion When Responses Are Misclassified

2020 ◽  
Author(s):  
Sumanta Adhya ◽  
Surupa Roy ◽  
Tathagata Banerjee
Keyword(s):  
Empirical Study ◽  
Finite Population ◽  
Asymptotic Properties ◽  
Auxiliary Variable ◽  
Binary Response ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Computationally Efficient ◽  
Extensive Simulation ◽  
Population Proportion

Abstract We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.

Altered brain network topology in left-behind children: A resting-state functional magnetic resonance imaging study

2016 ◽  
Vol 62 ◽  
pp. 89-99 ◽  
Author(s):  
Youjin Zhao ◽  
Meimei Du ◽  
Xin Gao ◽  
Yuan Xiao ◽  
Chandan Shah ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Network Topology ◽  
Resting State ◽  
Imaging Study ◽  
Brain Network ◽  
Magnetic Resonance Imaging Study ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Left Behind

scholarly journals Writing affects the brain network of reading in Chinese: A functional magnetic resonance imaging study

2012 ◽  
Vol 34 (7) ◽  
pp. 1670-1684 ◽  
Author(s):  
Fan Cao ◽  
Marianne Vu ◽  
Derek Ho Lung Chan ◽  
Jason M. Lawrence ◽  
Lindsay N. Harris ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Imaging Study ◽  
Brain Network ◽  
Magnetic Resonance Imaging Study ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Brain
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