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A Concentration Inequality based methodology for Sparse Covariance Estimation

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If you have a question about this talk, please contact Nicolai Baldin.

In this talk, we propose a general framework for covariance matrix estimation making use of concentration inequality-based confidence sets, and we specify this framework for the estimation of large sparse covariance matrices. The usage of nonasymptotic dimension-free confidence sets yields good theoretical performance for such sparse estimators given reasonable distributional assumptions. The proposed method merges past ideas including shrinkage, penalized, and threshold estimators. Through extensive simulations, it is demonstrated to have superior performance when compared with other such methods.

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