Comptes Rendus
Statistics
CLT and Lq errors in nonparametric functional regression
Comptes Rendus. Mathématique, Volume 345 (2007) no. 7, pp. 411-414.

We study a nonparametric functional regression model and we provide an asymptotic law with explicit constants under α-mixing assumptions. Then we establish both pointwise confidence bands for the regression operator and asymptotic Lq errors for its kernel estimator.

On étudiera dans cet article la normalité asymptotique de l'estimateur à noyau pour des données α-mélangeantes fonctionnelles. L'explicitation des constantes apparaissant dans la loi asymptotique permet d'établir des intervalles de confiance ponctuels pour l'opérateur de régression ainsi que l'expression des erreurs Lq.

Received:
Accepted:
Published online:
DOI: 10.1016/j.crma.2007.07.021

Laurent Delsol 1

1 L.S.P., Université Paul-Sabatier, 31062 Toulouse, France
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Laurent Delsol. CLT and $ {\mathbb{L}}^{q}$ errors in nonparametric functional regression. Comptes Rendus. Mathématique, Volume 345 (2007) no. 7, pp. 411-414. doi : 10.1016/j.crma.2007.07.021. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2007.07.021/

[1] J. Dedecker; C. Prieur New dependence coefficients. Examples and applications to statistics, Probab. Theory Related Fields, Volume 132 (2005), pp. 203-236

[2] F. Ferraty; A. Mas; P. Vieu Advances on nonparametric regression for functional data, ANZ Journal of Statistics (2007) (in press)

[3] F. Ferraty; P. Vieu Nonparametric Functional Data Analysis, Springer-Verlag, New York, 2006

[4] E. Liebscher Central limit theorem for α-mixing triangular arrays with applications to nonparametric statistics, Mathematical Methods of Statistics, Volume 10 (2001) no. 2, pp. 194-214

[5] E. Masry Nonparametric regression estimation for dependent functional data: asymptotic normality, Stochastic Process. Appl., Volume 115 (2005) no. 1, pp. 155-177

[6] E. Rio About the Lindeberg method for strongly mixing sequences, ESAIM, Volume 1 (1995), pp. 35-61

[7] E. Rio Théorie asymptotique des processus aléatoires faiblement dépendants, Mathématiques et applications, vol. 31, Springer-Verlag, Berlin, 2000

[8] M.P. Wand On exact L1 rates in nonparametric kernel regression, Scand. J. Statist., Volume 17 (1990) no. 3, pp. 251-256

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