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Multiple Matrix Gaussian Graphs Estimation.


ABSTRACT: Matrix-valued data, where the sampling unit is a matrix consisting of rows and columns of measurements, are emerging in numerous scientific and business applications. Matrix Gaussian graphical model is a useful tool to characterize the conditional dependence structure of rows and columns. In this article, we employ nonconvex penalization to tackle the estimation of multiple graphs from matrix-valued data under a matrix normal distribution. We propose a highly efficient nonconvex optimization algorithm that can scale up for graphs with hundreds of nodes. We establish the asymptotic properties of the estimator, which requires less stringent conditions and has a sharper probability error bound than existing results. We demonstrate the efficacy of our proposed method through both simulations and real functional magnetic resonance imaging analyses.

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC6261498 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Multiple Matrix Gaussian Graphs Estimation.

Zhu Yunzhang Y   Li Lexin L  

Journal of the Royal Statistical Society. Series B, Statistical methodology 20180614 5


Matrix-valued data, where the sampling unit is a matrix consisting of rows and columns of measurements, are emerging in numerous scientific and business applications. Matrix Gaussian graphical model is a useful tool to characterize the conditional dependence structure of rows and columns. In this article, we employ nonconvex penalization to tackle the estimation of multiple graphs from matrix-valued data under a matrix normal distribution. We propose a highly efficient nonconvex optimization alg  ...[more]

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