Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection

Autores
Bianco, Ana Maria; Boente Boente, Graciela Lina
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smoothing techniques are combined. Asymptotic results on the autoregression estimators are derived. Besides combining robust procedures with M-smoothers, predicted values for the series and detection residuals, which allow to detect anomalous data, are introduced. Robust cross-validation methods to select the smoothing parameter are presented as an alternative to the classical ones, which are sensitive to outlying observations. A Monte Carlo study is conducted to compare the performance of the proposed criteria. Finally, the asymptotic distribution of the autoregression parameter estimator is stated uniformly over the smoothing parameter.
Fil: Bianco, Ana Maria. 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: 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
Materia
ASYMPTOTIC PROPERTIES
CROSS-VALIDATION
FILTERING
PARTLY LINEAR AUTOREGRESSION
PREDICTION
RATE OF CONVERGENCE
ROBUST ESTIMATION
SMOOTHING TECHNIQUES
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:11336/125432
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/125432
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selectionBianco, Ana MariaBoente Boente, Graciela LinaASYMPTOTIC PROPERTIESCROSS-VALIDATIONFILTERINGPARTLY LINEAR AUTOREGRESSIONPREDICTIONRATE OF CONVERGENCEROBUST ESTIMATIONSMOOTHING TECHNIQUEShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smoothing techniques are combined. Asymptotic results on the autoregression estimators are derived. Besides combining robust procedures with M-smoothers, predicted values for the series and detection residuals, which allow to detect anomalous data, are introduced. Robust cross-validation methods to select the smoothing parameter are presented as an alternative to the classical ones, which are sensitive to outlying observations. A Monte Carlo study is conducted to compare the performance of the proposed criteria. Finally, the asymptotic distribution of the autoregression parameter estimator is stated uniformly over the smoothing parameter.Fil: Bianco, Ana Maria. 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: 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"; ArgentinaWiley Blackwell Publishing, Inc2007-03info: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/11336/125432Bianco, Ana Maria; Boente Boente, Graciela Lina; Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection; Wiley Blackwell Publishing, Inc; Journal Of Time Series Analysis; 28; 2; 3-2007; 274-3060143-9782CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/j.1467-9892.2006.00511.xinfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9892.2006.00511.xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-05T09:38:30Zoai:ri.conicet.gov.ar:11336/125432instacron: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:34982025-11-05 09:38:30.964CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
title Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
spellingShingle Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
Bianco, Ana Maria
ASYMPTOTIC PROPERTIES
CROSS-VALIDATION
FILTERING
PARTLY LINEAR AUTOREGRESSION
PREDICTION
RATE OF CONVERGENCE
ROBUST ESTIMATION
SMOOTHING TECHNIQUES
title_short Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
title_full Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
title_fullStr Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
title_full_unstemmed Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
title_sort Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
dc.creator.none.fl_str_mv Bianco, Ana Maria
Boente Boente, Graciela Lina
author Bianco, Ana Maria
author_facet Bianco, Ana Maria
Boente Boente, Graciela Lina
author_role author
author2 Boente Boente, Graciela Lina
author2_role author
dc.subject.none.fl_str_mv ASYMPTOTIC PROPERTIES
CROSS-VALIDATION
FILTERING
PARTLY LINEAR AUTOREGRESSION
PREDICTION
RATE OF CONVERGENCE
ROBUST ESTIMATION
SMOOTHING TECHNIQUES
topic ASYMPTOTIC PROPERTIES
CROSS-VALIDATION
FILTERING
PARTLY LINEAR AUTOREGRESSION
PREDICTION
RATE OF CONVERGENCE
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 In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smoothing techniques are combined. Asymptotic results on the autoregression estimators are derived. Besides combining robust procedures with M-smoothers, predicted values for the series and detection residuals, which allow to detect anomalous data, are introduced. Robust cross-validation methods to select the smoothing parameter are presented as an alternative to the classical ones, which are sensitive to outlying observations. A Monte Carlo study is conducted to compare the performance of the proposed criteria. Finally, the asymptotic distribution of the autoregression parameter estimator is stated uniformly over the smoothing parameter.
Fil: Bianco, Ana Maria. 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: 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
description In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smoothing techniques are combined. Asymptotic results on the autoregression estimators are derived. Besides combining robust procedures with M-smoothers, predicted values for the series and detection residuals, which allow to detect anomalous data, are introduced. Robust cross-validation methods to select the smoothing parameter are presented as an alternative to the classical ones, which are sensitive to outlying observations. A Monte Carlo study is conducted to compare the performance of the proposed criteria. Finally, the asymptotic distribution of the autoregression parameter estimator is stated uniformly over the smoothing parameter.
publishDate 2007
dc.date.none.fl_str_mv 2007-03
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/11336/125432
Bianco, Ana Maria; Boente Boente, Graciela Lina; Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection; Wiley Blackwell Publishing, Inc; Journal Of Time Series Analysis; 28; 2; 3-2007; 274-306
0143-9782
CONICET Digital
CONICET
url http://hdl.handle.net/11336/125432
identifier_str_mv Bianco, Ana Maria; Boente Boente, Graciela Lina; Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection; Wiley Blackwell Publishing, Inc; Journal Of Time Series Analysis; 28; 2; 3-2007; 274-306
0143-9782
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1111/j.1467-9892.2006.00511.x
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9892.2006.00511.x
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 Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
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_ 1847976882935431168
score 13.087074

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