Title :

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

Author(s) :

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

English keyword(s) :

Nonparametric regression estimation
asymptotic normality
kernel estimator
strongly mixing random field

HAL domain(s) :

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

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Comment :

20 pages

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL