Adaptive Covariance Estimation with model selection
Statistics Theory
2012-03-05 v1 Statistics Theory
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.
Cite
@article{arxiv.1203.0107,
title = {Adaptive Covariance Estimation with model selection},
author = {Rolando Biscay and Hélène Lescornel and Jean-Michel Loubes},
journal= {arXiv preprint arXiv:1203.0107},
year = {2012}
}