Estimation of large covariance matrices via free deconvolution: computational and statistical aspects

05/09/2023

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The estimation of large covariance matrices has a high dimensional bias. Correcting for this bias can be reformulated via the tool of Free Probability Theory as a free deconvolution. The goal of this work is a computational and statistical resolution of this problem. Our approach is based on complex-analytic methods methods to invert S-transforms. In particular, one needs a theoretical understanding of the Riemann surfaces where multivalued S transforms live and an efficient computational scheme.

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Estimation of large covariance matrices via free deconvolution: computational and statistical aspects

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Estimation of large covariance matrices via free deconvolution: computational and statistical aspects

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