Afrika Statistika

Moderate deviations principle for the kernel estimator of nonrandom regression functions

Abdelkader MOKKADEM and Mariane PELLETIER

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text


The aim of this paper is to provide pointwise and uniform moderate deviations principles for the kernel estimator of a nonrandom regression function. Moreover, we give an application of these moderate deviations principles to the construction of confidence regions for the regression function.


L'objectif de cet article est de donner les principes de déviations modérées, ponctuels et uniformes, satisfaits par l'estimateur à noyau d'une fonction de régression déterministe. De plus, nous donnons une application de ces principes de déviations modérées à la construction de régions de confiance pour la fonction de régression.

Article information

Afr. Stat., Volume 11, Number 2 (2016), 995-1021.

First available in Project Euclid: 20 January 2017

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


MOKKADEM, Abdelkader; PELLETIER, Mariane. Moderate deviations principle for the kernel estimator of nonrandom regression functions. Afr. Stat. 11 (2016), no. 2, 995--1021. doi:10.16929/as/2016.995.89.

Export citation