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Nonparametric estimation of the density of the regression noise
[Estimation non-paramétrique de la loi des erreurs dans un modèle de régression]
Comptes Rendus. Mathématique, Tome 346 (2008) no. 7-8, pp. 461-466.

Cette Note présente un estimateur de la loi de l'erreur dans un modèle de régression homoscédastique, basé sur des techniques de sélection de modèle, et propose une majoration du risque quadratique intégré.

This Note presents an estimator of the density of the error in a homoscedastic regression model, based on model selection methods, and propose a bound for the quadratic integrated risk.

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DOI : 10.1016/j.crma.2008.02.021
Plancade, Sandra 1

1 MAP5, UMR 8145, Université Paris Descartes, 45, rue des Saints-Pères, 75270 Paris cedex 06, France
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Plancade, Sandra. Nonparametric estimation of the density of the regression noise. Comptes Rendus. Mathématique, Tome 346 (2008) no. 7-8, pp. 461-466. doi : 10.1016/j.crma.2008.02.021. http://www.numdam.org/articles/10.1016/j.crma.2008.02.021/

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[6] C. Lacour, Adaptative estimation of the transition density of a particular hidden Markov chain, J. Multivariate Anal. 2006, in press, available online; arXiv: math/0611681v1 [math.ST], hal-00115612, version 1

[7] Massart, P. Concentration inequalities and model selection, July 6–23, 2003 (Lecture Notes in Mathematics), Volume vol. 1896 (2007)

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