Adaptive estimation of an additive regression function from weakly dependent data - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2011

Adaptive estimation of an additive regression function from weakly dependent data

Résumé

A $d$-dimensional nonparametric additive regression model with dependent observations is considered. Using the marginal integration and the methods of wavelets, we develop a new adaptive estimator for a component of the additive regression function. Its asymptotic properties are investigated via the minimax approach under the $mathbb{L}_2$ risk over Besov balls. We prove that it attains a sharp rate of convergence, close to the one obtained in the one-dimensional case. In particular, it is both independent of $d$ and slightly deteriorated by the dependence of the observations.
Fichier principal
Vignette du fichier
add3.pdf (188.18 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-00641912 , version 1 (17-11-2011)
hal-00641912 , version 2 (06-08-2012)

Identifiants

  • HAL Id : hal-00641912 , version 1

Citer

Christophe Chesneau, Jalal M. Fadili, Bertrand Maillot. Adaptive estimation of an additive regression function from weakly dependent data. 2011. ⟨hal-00641912v1⟩
136 Consultations
174 Téléchargements

Partager

Gmail Facebook X LinkedIn More