Robust estimators in semi-functional partial linear regression models

Partial linear models have been adapted to deal with functional covariates to capture both the advantages of a semi-linear modelling and those of nonparametric modelling for functional data. It is easy to see that the estimation procedures for these models are highly sensitive to the presence of eve...

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Detalhes bibliográficos
Autores: Boente Boente, Graciela Lina, Vahnovan, Alejandra Valeria
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:Argentina
Recursos:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/55556
Acesso em linha:http://hdl.handle.net/11336/55556
Access Level:acceso abierto
Palavra-chave:Functional Data
Kernel Smoothers
Partial Linear Models
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
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spelling Robust estimators in semi-functional partial linear regression modelsBoente Boente, Graciela LinaVahnovan, Alejandra ValeriaFunctional DataKernel SmoothersPartial Linear ModelsRobust Estimationhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Partial linear models have been adapted to deal with functional covariates to capture both the advantages of a semi-linear modelling and those of nonparametric modelling for functional data. It is easy to see that the estimation procedures for these models are highly sensitive to the presence of even a small proportion of outliers in the data. To solve the problem of atypical observations when the covariates of the nonparametric component are functional, robust estimates for the regression parameter and regression operator are introduced. Consistency results of the robust estimators and the asymptotic distribution of the regression parameter estimator are studied. The reported numerical experiments show that the resulting estimators have good robustness properties. The benefits of considering robust estimators is also illustrated on a real data set where the robust fit reveals the presence of influential outliers.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: Vahnovan, Alejandra Valeria. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Inc2017-02info: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/11336/55556Boente Boente, Graciela Lina; Vahnovan, Alejandra Valeria; Robust estimators in semi-functional partial linear regression models; Elsevier Inc; Journal Of Multivariate Analysis; 154; 2-2017; 59-840047-259XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2016.10.005info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0047259X16301178?via%3Dihubinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:45:15Zoai:ri.conicet.gov.ar:11336/55556instacron: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:34982024-05-08 13:45:15.985CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust estimators in semi-functional partial linear regression models
title Robust estimators in semi-functional partial linear regression models
spellingShingle Robust estimators in semi-functional partial linear regression models
Boente Boente, Graciela Lina
Functional Data
Kernel Smoothers
Partial Linear Models
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
title_short Robust estimators in semi-functional partial linear regression models
title_full Robust estimators in semi-functional partial linear regression models
title_fullStr Robust estimators in semi-functional partial linear regression models
title_full_unstemmed Robust estimators in semi-functional partial linear regression models
title_sort Robust estimators in semi-functional partial linear regression models
dc.creator.none.fl_str_mv Boente Boente, Graciela Lina
Vahnovan, Alejandra Valeria
author Boente Boente, Graciela Lina
author_facet Boente Boente, Graciela Lina
Vahnovan, Alejandra Valeria
author_role author
author2 Vahnovan, Alejandra Valeria
author2_role author
dc.subject.none.fl_str_mv Functional Data
Kernel Smoothers
Partial Linear Models
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
topic Functional Data
Kernel Smoothers
Partial Linear Models
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
description Partial linear models have been adapted to deal with functional covariates to capture both the advantages of a semi-linear modelling and those of nonparametric modelling for functional data. It is easy to see that the estimation procedures for these models are highly sensitive to the presence of even a small proportion of outliers in the data. To solve the problem of atypical observations when the covariates of the nonparametric component are functional, robust estimates for the regression parameter and regression operator are introduced. Consistency results of the robust estimators and the asymptotic distribution of the regression parameter estimator are studied. The reported numerical experiments show that the resulting estimators have good robustness properties. The benefits of considering robust estimators is also illustrated on a real data set where the robust fit reveals the presence of influential outliers.
publishDate 2017
dc.date.none.fl_str_mv 2017-02
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/11336/55556
Boente Boente, Graciela Lina; Vahnovan, Alejandra Valeria; Robust estimators in semi-functional partial linear regression models; Elsevier Inc; Journal Of Multivariate Analysis; 154; 2-2017; 59-84
0047-259X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/55556
identifier_str_mv Boente Boente, Graciela Lina; Vahnovan, Alejandra Valeria; Robust estimators in semi-functional partial linear regression models; Elsevier Inc; Journal Of Multivariate Analysis; 154; 2-2017; 59-84
0047-259X
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.jmva.2016.10.005
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0047259X16301178?via%3Dihub
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 Inc
publisher.none.fl_str_mv Elsevier Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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
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score 15,812429

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