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Unified Framework to Regularized Covariance Estimation in Scaled Gaussian Models
2012
IEEE Transactions on Signal Processing
We consider regularized covariance estimation in scaled Gaussian settings, e.g., elliptical distributions, compound-Gaussian processes and spherically invariant random vectors. Asymptotically in the number of samples, the classical maximum likelihood (ML) estimate is optimal under different criteria and can be efficiently computed even though the optimization is nonconvex. We propose a unified framework for regularizing this estimate in order to improve its finite sample performance. Our
doi:10.1109/tsp.2011.2170685
fatcat:dsegehcqlvaqdm7tovbcbfjnzu