Covariance Estimation for Matrix-valued Data

11 Apr 2020Yichi ZhangWeining ShenDehan Kong

Covariance estimation for matrix-valued data has received an increasing interest in applications including neuroscience and environmental studies. Unlike previous works that rely heavily on matrix normal distribution assumption and the requirement of fixed matrix size, we propose a class of distribution-free regularized covariance estimation methods for high-dimensional matrix data under a separability condition and a bandable covariance structure... (read more)

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