Asymptotic normality of recursive estimators under strong mixing conditions

Aboubacar Amiri ()

Statistical Inference for Stochastic Processes, 2013, vol. 16, issue 2, 96 pages

Abstract: 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. Copyright Springer Science+Business Media Dordrecht 2013

Keywords: Recursive kernel estimators; Regression function; Strong mixing processes; Asymptotic normality; 62G05; 62G07; 62G08 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s11203-013-9078-x (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sistpr:v:16:y:2013:i:2:p:81-96

Ordering information: This journal article can be ordered from
http://www.springer. ... ty/journal/11203/PS2

DOI: 10.1007/s11203-013-9078-x

Access Statistics for this article

Statistical Inference for Stochastic Processes is currently edited by Denis Bosq, Yury A. Kutoyants and Marc Hallin

More articles in Statistical Inference for Stochastic Processes from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().