The Empirical Likelihood method applied to covariance matrix estimation

Authors:
Frédéric Pascal;Hugo Harari-Kermadec;Pascal Larzabal
Affiliations:
SONDRA/Supelec, 3 rue Joliot-Curie, F-91192 Gif-sur-Yvette Cedex, France;ENS Cachan, 61 Av. du Pdt Wilson, F-94235 Cachan Cedex, France;SATIE, ENS Cachan, CNRS, UniverSud, 61 Av. du Pdt Wilson, F-94235 Cachan Cedex, France
Venue:
Signal Processing
Year:
2010

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Abstract

This paper presents a new estimation scheme for signal processing problems in unknown noise field. The Empirical Likelihood has been introduced in the mathematical community, but, surprisingly, it is still unknown in the signal processing community. This estimation method is an alternative to estimate unknown parameters without using a model for the probability density function. The aim of this paper is twofold: first, the Empirical Likelihood theory is presented and revisited thanks to the moment method. Its properties are derived. Second, to emphasize all the potentiality of this method, we address the problem of Toeplitz matrix estimation: this leads us to obtain improved estimates in comparison to conventional ones, as shown in simulations.