Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence

We consider automatic data‐driven density, regression and autoregression estimates, based on any random bandwidth selector h/T. We show that in a first‐order asymptotic approximation they behave as well as the related estimates obtained with the “optimal” bandwidth hT as long as hT/hT → 1 in probabi...

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Detalles Bibliográficos
Publicado: 1995
Materias:
62M10.
autoregression models
Data‐driven bandwidth selectors
density estimation
kernel estimates
nonparametric regression
Primary 62G05
secondary 62G20
α‐mixing processes
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03195724_v23_n4_p383_Boente
http://hdl.handle.net/20.500.12110/paper_03195724_v23_n4_p383_Boente
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
Descripción
Sumario:We consider automatic data‐driven density, regression and autoregression estimates, based on any random bandwidth selector h/T. We show that in a first‐order asymptotic approximation they behave as well as the related estimates obtained with the “optimal” bandwidth hT as long as hT/hT → 1 in probability. The results are obtained for dependent observations; some of them are also new for independent observations. Copyright © 1995 Statistical Society of Canada

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