Adaptive Covariance Estimation with model selection

Rolando Biscay; Hélène Lescornel; Jean-Michel Loubes

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arXiv · 1203.0107

Adaptive Covariance Estimation with model selection

Rolando Biscay·Hélène Lescornel·Jean-Michel Loubes

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Abstract

We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud.

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Rolando Biscay, Hélène Lescornel, Jean-Michel Loubes. 2012-03-01. Adaptive Covariance Estimation with model selection. https://arxiv.org/abs/1203.0107

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