Robust Shrinkage Estimation of High-Dimensional Covariance Matrices

We address high dimensional covariance estimation for elliptical distributed samples, which are also known as spherically invariant random vectors (SIRV) or compound-Gaussian processes. Specifically we consider shrinkage methods that are suitable for high dimensional problems with a small number of...

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Veröffentlicht in:	IEEE transactions on signal processing 2011-09, Vol.59 (9), p.4097-4107
Hauptverfasser:	Yilun Chen, Wiesel, A., Hero, A. O.
Format:	Artikel
Sprache:	eng
Schlagworte:	Activity/intrusion detection Applied sciences Coefficients Convergence Covariance covariance estimation Covariance matrix Detection, estimation, filtering, equalization, prediction elliptical distribution Errors Exact sciences and technology Exact solutions Information, signal and communications theory Invariants large p small n Mathematical analysis Matrices Maximum likelihood estimation robust estimation Robustness Shrinkage shrinkage methods Signal and communications theory Signal processing Signal, noise Studies Telecommunications and information theory wireless sensor network Wireless sensor networks
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Beschreibung
Zusammenfassung:	We address high dimensional covariance estimation for elliptical distributed samples, which are also known as spherically invariant random vectors (SIRV) or compound-Gaussian processes. Specifically we consider shrinkage methods that are suitable for high dimensional problems with a small number of samples (large p small n ). We start from a classical robust covariance estimator [Tyler (1987)], which is distribution-free within the family of elliptical distribution but inapplicable when n
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2011.2138698