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Asymptotic normality of recursive estimators under strong mixing conditions

Content Provider	Springer Nature Link
Author	Amiri, Aboubacar
Copyright Year	2013
Abstract	The main purpose of this paper is to estimate the regression function by using a recursive nonparametric kernel approach. We derive the asymptotic normality for a general class of recursive kernel estimate of the regression function, under strong mixing conditions. Our purpose is to extend the work of Roussas and Tran (Ann Stat 20:98–120, 1992) concerning the Devroye–Wagner estimate.Dans ce papier, nous nous intéressons à l’estimation de la fonction de régression par une approche non-paramétrique par noyau. Nous établissons la normalité asymptotique, pour une famille générale d’estimateurs récursifs à noyau de la fonction de régression, sous une hypothèse de forte mélangence. Notre rsultat généralise ainsi le résulttat de Roussas and Tran (Ann Stat 20:98–120, 1992) sur l’estimateur de Devroye–Wagner.
Ending Page	96
Page Count	16
Starting Page	81
File Format	PDF
ISSN	13870874
e-ISSN	15729311
Journal	Statistical Inference for Stochastic Processes
Issue Number	2
Volume Number	16
Language	English
Publisher	Springer Netherlands
Publisher Date	2013-06-08
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Density estimation Strong mixing processes Asymptotic normality Estimation Nonparametric regression Recursive kernel estimators Regression function Probability Theory and Stochastic Processes
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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