Robust estimation for nonparametric generalized regression

Autores
Bianco, Ana Maria; Boente Boente, Graciela Lina; Sombielle, Susana
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper focuses on nonparametric regression estimation for the parameters of a discrete or continuous distribution, such as the Poisson or Gamma distributions, when anomalous data are present. The proposal is a natural extension of robust methods developed in the setting of parametric generalized linear models. Robust estimators bounding either large values of the deviance or of the Pearson residuals are introduced and their asymptotic behaviour is derived. Through a Monte Carlo study, for the Poisson and Gamma distributions, the finite properties of the proposed procedures are investigated and their performance is compared with that of the classical ones. A resistant cross-validation method to choose the smoothing parameter is also considered.
Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Sombielle, Susana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Universidad Tecnologica Nacional; Argentina
Materia
Asymptotic Properties
Nonparametric Generalized Regression
Robust Estimation
Smoothing Techniques
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/14907
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Robust estimation for nonparametric generalized regressionBianco, Ana MariaBoente Boente, Graciela LinaSombielle, SusanaAsymptotic PropertiesNonparametric Generalized RegressionRobust EstimationSmoothing Techniqueshttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1This paper focuses on nonparametric regression estimation for the parameters of a discrete or continuous distribution, such as the Poisson or Gamma distributions, when anomalous data are present. The proposal is a natural extension of robust methods developed in the setting of parametric generalized linear models. Robust estimators bounding either large values of the deviance or of the Pearson residuals are introduced and their asymptotic behaviour is derived. Through a Monte Carlo study, for the Poisson and Gamma distributions, the finite properties of the proposed procedures are investigated and their performance is compared with that of the classical ones. A resistant cross-validation method to choose the smoothing parameter is also considered.Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Sombielle, Susana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Universidad Tecnologica Nacional; ArgentinaElsevier2011-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14907Bianco, Ana Maria; Boente Boente, Graciela Lina; Sombielle, Susana; Robust estimation for nonparametric generalized regression; Elsevier; Statistics & Probability Letters; 81; 12; 12-2011; 1986-19940167-7152enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167715211002719info:eu-repo/semantics/altIdentifier/doi/10.1016/j.spl.2011.08.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:47:36Zoai:ri.conicet.gov.ar:11336/14907instacron: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:34982025-09-29 09:47:36.383CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust estimation for nonparametric generalized regression
title Robust estimation for nonparametric generalized regression
spellingShingle Robust estimation for nonparametric generalized regression
Bianco, Ana Maria
Asymptotic Properties
Nonparametric Generalized Regression
Robust Estimation
Smoothing Techniques
title_short Robust estimation for nonparametric generalized regression
title_full Robust estimation for nonparametric generalized regression
title_fullStr Robust estimation for nonparametric generalized regression
title_full_unstemmed Robust estimation for nonparametric generalized regression
title_sort Robust estimation for nonparametric generalized regression
dc.creator.none.fl_str_mv Bianco, Ana Maria
Boente Boente, Graciela Lina
Sombielle, Susana
author Bianco, Ana Maria
author_facet Bianco, Ana Maria
Boente Boente, Graciela Lina
Sombielle, Susana
author_role author
author2 Boente Boente, Graciela Lina
Sombielle, Susana
author2_role author
author
dc.subject.none.fl_str_mv Asymptotic Properties
Nonparametric Generalized Regression
Robust Estimation
Smoothing Techniques
topic Asymptotic Properties
Nonparametric Generalized Regression
Robust Estimation
Smoothing Techniques
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper focuses on nonparametric regression estimation for the parameters of a discrete or continuous distribution, such as the Poisson or Gamma distributions, when anomalous data are present. The proposal is a natural extension of robust methods developed in the setting of parametric generalized linear models. Robust estimators bounding either large values of the deviance or of the Pearson residuals are introduced and their asymptotic behaviour is derived. Through a Monte Carlo study, for the Poisson and Gamma distributions, the finite properties of the proposed procedures are investigated and their performance is compared with that of the classical ones. A resistant cross-validation method to choose the smoothing parameter is also considered.
Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Sombielle, Susana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Universidad Tecnologica Nacional; Argentina
description This paper focuses on nonparametric regression estimation for the parameters of a discrete or continuous distribution, such as the Poisson or Gamma distributions, when anomalous data are present. The proposal is a natural extension of robust methods developed in the setting of parametric generalized linear models. Robust estimators bounding either large values of the deviance or of the Pearson residuals are introduced and their asymptotic behaviour is derived. Through a Monte Carlo study, for the Poisson and Gamma distributions, the finite properties of the proposed procedures are investigated and their performance is compared with that of the classical ones. A resistant cross-validation method to choose the smoothing parameter is also considered.
publishDate 2011
dc.date.none.fl_str_mv 2011-12
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/11336/14907
Bianco, Ana Maria; Boente Boente, Graciela Lina; Sombielle, Susana; Robust estimation for nonparametric generalized regression; Elsevier; Statistics & Probability Letters; 81; 12; 12-2011; 1986-1994
0167-7152
url http://hdl.handle.net/11336/14907
identifier_str_mv Bianco, Ana Maria; Boente Boente, Graciela Lina; Sombielle, Susana; Robust estimation for nonparametric generalized regression; Elsevier; Statistics & Probability Letters; 81; 12; 12-2011; 1986-1994
0167-7152
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167715211002719
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.spl.2011.08.007
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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_ 1844613482627989504
score 13.069144

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