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Comptes Rendus Mathématique
Volume 339, n° 10
pages 713-716 (novembre 2004)
Doi : 10.1016/j.crma.2004.09.024
Received : 20 December 2003 ;  accepted : 27 September 2004
Estimation nonparamétrique multidimensionnelle des dérivées de la régression
Nonparametric, multidimensional estimation of regression derivatives.
 

David Blondin
L.S.T.A., université Paris VI, 175, rue du Chevaleret, 75013 Paris, France 

Résumé

Nous présentons des estimateurs à noyau de type Nadaraya-Watson des dérivées de la régression dans un cadre multidimensionnel. En sʼinspirant dʼune méthode originale basée sur la théorie moderne des processus empiriques [Deheuvels et Mason, Stat. Inference Stoch. Process. 7 (2004)], nous établissons des lois limites concernant la déviation maximale de ces estimateurs. Pour citer cet article : D. Blondin, C. R. Acad. Sci. Paris, Ser. I 339 (2004).

The full text of this article is available in PDF format.
Abstract

We establish uniform consistency rates for Nadaraya-Watson kernel-type estimators of the regression derivatives in a multidimensional framework. Our methods are based upon modern empirical process theory in the spirit of Deheuvels and Mason [Stat. Inference Stoch. Process. 7 (2004)] with respect to uniform deviations of nonparametric estimators. To cite this article: D. Blondin, C. R. Acad. Sci. Paris, Ser. I 339 (2004).

The full text of this article is available in PDF format.


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