Titre :

Asymptotic normality of kernel estimates in a regression model for random fields

Auteur(s) :

El Machkouri, Mohamed [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Stoica, Radu [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Mot(s)-clé(s) en anglais :

Nonparametric regression estimation
asymptotic normality
kernel estimator
strongly mixing random field

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]

Résumé en anglais : [en]

We establish the asymptotic normality of the regression estimator in a fixed-design setting when the errors are given by a field of dependent random variables. The result applies to martingale-difference or strongly mixing ...
Lire la suite >We establish the asymptotic normality of the regression estimator in a fixed-design setting when the errors are given by a field of dependent random variables. The result applies to martingale-difference or strongly mixing random fields. On this basis, a statistical test that can be applied to image analysis is also presented.Lire moins >

Langue :

Anglais

Commentaire :

20 pages

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL