The Empirical Likelihood method applied to covariance matrix estimation

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.
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Journal articles
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https://hal-supelec.archives-ouvertes.fr/hal-00556225
Contributor : Anne-Hélène Picot <>
Submitted on : Saturday, January 15, 2011 - 10:14:47 PM
Last modification on : Wednesday, July 24, 2019 - 8:36:03 PM

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Frédéric Pascal, Hugo Harari-Kermadec, Pascal Larzabal. The Empirical Likelihood method applied to covariance matrix estimation. Signal Processing, Elsevier, 2010, 90 (2), pp. 566-578. ⟨10.1016/j.sigpro.2009.07.028⟩. ⟨hal-00556225⟩

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