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Model selection for estimating the non zero components of aGaussian vector

Published online by Cambridge University Press:  09 March 2006

Sylvie Huet
Sylvie Huet*
Affiliation:
INRA, MIA, 78352 Jouy-en-Josas Cedex, France; huet@banian.jouy.inra.fr
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Abstract

We propose a method based on a penalised likelihood criterion, for estimating the number on non-zero components of the mean of a Gaussian vector. Following the work of Birgé and Massart in Gaussian model selection, we choose the penalty function such that the resulting estimator minimises the Kullback risk.

Keywords

Kullback riskmodel selectionpenalised likelihood criteria.
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 10 , February 2006 , pp. 164 - 183
Copyright
© EDP Sciences, SMAI, 2006

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