Unbiased risk estimation method for covariance estimation

Hélène Lescornel; Jean-Michel Loubes; Claudie Chabriac

ESAIM: Probability and Statistics (2014)

  • Volume: 18, page 251-264
  • ISSN: 1292-8100

Abstract

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We consider a model selection estimator of the covariance of a random process. Using the Unbiased Risk Estimation (U.R.E.) method, we build an estimator of the risk which allows to select an estimator in a collection of models. Then, we present an oracle inequality which ensures that the risk of the selected estimator is close to the risk of the oracle. Simulations show the efficiency of this methodology.

How to cite

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Lescornel, Hélène, Loubes, Jean-Michel, and Chabriac, Claudie. "Unbiased risk estimation method for covariance estimation." ESAIM: Probability and Statistics 18 (2014): 251-264. <http://eudml.org/doc/274370>.

@article{Lescornel2014,
abstract = {We consider a model selection estimator of the covariance of a random process. Using the Unbiased Risk Estimation (U.R.E.) method, we build an estimator of the risk which allows to select an estimator in a collection of models. Then, we present an oracle inequality which ensures that the risk of the selected estimator is close to the risk of the oracle. Simulations show the efficiency of this methodology.},
author = {Lescornel, Hélène, Loubes, Jean-Michel, Chabriac, Claudie},
journal = {ESAIM: Probability and Statistics},
keywords = {covariance estimation; model selection; U.R.E. method},
language = {eng},
pages = {251-264},
publisher = {EDP-Sciences},
title = {Unbiased risk estimation method for covariance estimation},
url = {http://eudml.org/doc/274370},
volume = {18},
year = {2014},
}

TY - JOUR
AU - Lescornel, Hélène
AU - Loubes, Jean-Michel
AU - Chabriac, Claudie
TI - Unbiased risk estimation method for covariance estimation
JO - ESAIM: Probability and Statistics
PY - 2014
PB - EDP-Sciences
VL - 18
SP - 251
EP - 264
AB - We consider a model selection estimator of the covariance of a random process. Using the Unbiased Risk Estimation (U.R.E.) method, we build an estimator of the risk which allows to select an estimator in a collection of models. Then, we present an oracle inequality which ensures that the risk of the selected estimator is close to the risk of the oracle. Simulations show the efficiency of this methodology.
LA - eng
KW - covariance estimation; model selection; U.R.E. method
UR - http://eudml.org/doc/274370
ER -

References

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