A Random Matrix--Theoretic Approach to Handling Singular Covariance Estimates
Disciplines
Text
English
ID: <
10670/1.jwcf56>
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