On a robust local estimator for the scale function in heteroscedastic nonparametric regression

Autores
Boente Boente, Graciela Lina; Ruiz, Marcelo; Zamar, Ruben Horacio
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local M-estimators of the scale function based on consecutive differences of the responses, for fixed designs are considered. Under mild regularity conditions, the asymptotic behavior of the local M-estimators for general weight functions is derived.
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Ruiz, Marcelo. Universidad Nacional de Río Cuarto; Argentina
Fil: Zamar, Ruben Horacio. University of British Columbia; Canadá
Materia
Heteroscedasticity
Local M-Estimators
Nonparametric Regression
Robust Estimation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/69114
Acceder
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network_name_str CONICET Digital (CONICET)
spelling On a robust local estimator for the scale function in heteroscedastic nonparametric regressionBoente Boente, Graciela LinaRuiz, MarceloZamar, Ruben HoracioHeteroscedasticityLocal M-EstimatorsNonparametric RegressionRobust Estimationhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local M-estimators of the scale function based on consecutive differences of the responses, for fixed designs are considered. Under mild regularity conditions, the asymptotic behavior of the local M-estimators for general weight functions is derived.Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Ruiz, Marcelo. Universidad Nacional de Río Cuarto; ArgentinaFil: Zamar, Ruben Horacio. University of British Columbia; CanadáElsevier Science2010-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/69114Boente Boente, Graciela Lina; Ruiz, Marcelo; Zamar, Ruben Horacio; On a robust local estimator for the scale function in heteroscedastic nonparametric regression; Elsevier Science; Statistics & Probability Letters; 80; 15-16; 8-2010; 1185-11950167-7152CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.spl.2010.03.015info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167715210000957info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:44Zoai:ri.conicet.gov.ar:11336/69114instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:58:45.14CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On a robust local estimator for the scale function in heteroscedastic nonparametric regression
title On a robust local estimator for the scale function in heteroscedastic nonparametric regression
spellingShingle On a robust local estimator for the scale function in heteroscedastic nonparametric regression
Boente Boente, Graciela Lina
Heteroscedasticity
Local M-Estimators
Nonparametric Regression
Robust Estimation
title_short On a robust local estimator for the scale function in heteroscedastic nonparametric regression
title_full On a robust local estimator for the scale function in heteroscedastic nonparametric regression
title_fullStr On a robust local estimator for the scale function in heteroscedastic nonparametric regression
title_full_unstemmed On a robust local estimator for the scale function in heteroscedastic nonparametric regression
title_sort On a robust local estimator for the scale function in heteroscedastic nonparametric regression
dc.creator.none.fl_str_mv Boente Boente, Graciela Lina
Ruiz, Marcelo
Zamar, Ruben Horacio
author Boente Boente, Graciela Lina
author_facet Boente Boente, Graciela Lina
Ruiz, Marcelo
Zamar, Ruben Horacio
author_role author
author2 Ruiz, Marcelo
Zamar, Ruben Horacio
author2_role author
author
dc.subject.none.fl_str_mv Heteroscedasticity
Local M-Estimators
Nonparametric Regression
Robust Estimation
topic Heteroscedasticity
Local M-Estimators
Nonparametric Regression
Robust Estimation
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local M-estimators of the scale function based on consecutive differences of the responses, for fixed designs are considered. Under mild regularity conditions, the asymptotic behavior of the local M-estimators for general weight functions is derived.
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Ruiz, Marcelo. Universidad Nacional de Río Cuarto; Argentina
Fil: Zamar, Ruben Horacio. University of British Columbia; Canadá
description When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local M-estimators of the scale function based on consecutive differences of the responses, for fixed designs are considered. Under mild regularity conditions, the asymptotic behavior of the local M-estimators for general weight functions is derived.
publishDate 2010
dc.date.none.fl_str_mv 2010-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/69114
Boente Boente, Graciela Lina; Ruiz, Marcelo; Zamar, Ruben Horacio; On a robust local estimator for the scale function in heteroscedastic nonparametric regression; Elsevier Science; Statistics & Probability Letters; 80; 15-16; 8-2010; 1185-1195
0167-7152
CONICET Digital
CONICET
url http://hdl.handle.net/11336/69114
identifier_str_mv Boente Boente, Graciela Lina; Ruiz, Marcelo; Zamar, Ruben Horacio; On a robust local estimator for the scale function in heteroscedastic nonparametric regression; Elsevier Science; Statistics & Probability Letters; 80; 15-16; 8-2010; 1185-1195
0167-7152
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.spl.2010.03.015
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167715210000957
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269539205644288
score 13.13397

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