Optimal and superoptimal convergence rate of the local linear estimator of nonparametric regression function in continuous time

Jia Shen; Shuguang Sun

doi:10.1016/j.crma.2009.12.005

Statistics

Optimal and superoptimal convergence rate of the local linear estimator of nonparametric regression function in continuous time
[L'optimalité et la suroptimalité du lissage localement linéaire de la fonction de régression]

Présenté par : Paul Deveuvels

Jia Shen ¹ ; Shuguang Sun ¹

¹ Department of Statistics, Fudan University, Shanghai 200433, China

Comptes Rendus. Mathématique, Volume 348 (2010) no. 3-4, pp. 211-215.

Résumé
Abstract

Nous étudions l'estimation non paramétrique de la fonction de régression par le lissage localement linéaire au sens d'erreur quadratique asymptotique. Sous des conditions de forte mélangeance et d'irrégularité, nous obtenons des vitesses de convergence optimale et suroptimale de l'estimateur pour l'erreur quadratique asymptotique.

We consider the estimation problem of the nonparametric regression in continuous time by the local linear estimator in the asymptotic quadratic error sense. In suitable conditions of strongly mixing and that of irregularity, we obtained optimal and superoptimal convergence rate of the estimator.

Reçu le : 2009-06-09
Accepté le : 2009-12-04
Publié le : 2009-12-30

DOI : 10.1016/j.crma.2009.12.005

Affiliations des auteurs :

Jia Shen ¹ ; Shuguang Sun ¹

¹ Department of Statistics, Fudan University, Shanghai 200433, China

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     title = {Optimal and superoptimal convergence rate of the local linear estimator of nonparametric regression function in continuous time},
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     pages = {211--215},
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     doi = {10.1016/j.crma.2009.12.005},
     language = {en},
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Jia Shen; Shuguang Sun. Optimal and superoptimal convergence rate of the local linear estimator of nonparametric regression function in continuous time. Comptes Rendus. Mathématique, Volume 348 (2010) no. 3-4, pp. 211-215. doi : 10.1016/j.crma.2009.12.005. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2009.12.005/

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Cité par Sources :

^☆ Supported partly by Natural Science Foundation of China (Project Number: 70832005) and Shanghai Leading Academic Discipline Project, Project Number: B210.

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