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ID: <

10670/1.jwcf56

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A Random Matrix--Theoretic Approach to Handling Singular Covariance Estimates

Abstract

In many practical situations we would like to estimate the covariance matrix of a set of variables from an insufficient amount of data. More specifically, if we have a set of $N$ independent, identically distributed measurements of an $M$ dimensional random vector the maximum likelihood estimate is the sample covariance matrix. Here we consider the case where $N Comment: Submitted to Transactions on Information Theory

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