Estimation of the marginal location under a partially linear model with missing responses

In this paper, we consider a semiparametric partially linear regression model where there are missing data in the response. We propose robust Fisher-consistent estimators for the regression parameter, for the regression function and for the marginal location parameter of the response variable. A rob...

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Detalles Bibliográficos
Autores: Bianco, Ana Maria, Boente Boente, Graciela Lina, González Manteiga, Wenceslao, Pérez González, Ana
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2010
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/160246
Acceso en línea:http://hdl.handle.net/11336/160246
Access Level:acceso abierto
Palabra clave:Fisher--consistency
Kernel Weights
M-location Functionals
Missing at Random
Nonparametric Regression
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
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spelling Estimation of the marginal location under a partially linear model with missing responsesBianco, Ana MariaBoente Boente, Graciela LinaGonzález Manteiga, WenceslaoPérez González, AnaFisher--consistencyKernel WeightsM-location FunctionalsMissing at RandomNonparametric RegressionRobust Estimationhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this paper, we consider a semiparametric partially linear regression model where there are missing data in the response. We propose robust Fisher-consistent estimators for the regression parameter, for the regression function and for the marginal location parameter of the response variable. A robust cross-validation method is briefly discussed, although, from our numerical results, the marginal estimators seem not to be sensitive to the bandwidth parameter. Finally, a Monte Carlo study is carried out to compare the performance of the robust proposed estimators among themselves and also with the classical ones, for normal and contaminated samples, under different missing data models. An example based on a real data set is also discussed.Fil: Bianco, Ana Maria. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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"; ArgentinaFil: González Manteiga, Wenceslao. Universidad de Santiago de Compostela; EspañaFil: Pérez González, Ana. Universidad de Vigo; EspañaElsevier Science2010-02info: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/11336/160246Bianco, Ana Maria; Boente Boente, Graciela Lina; González Manteiga, Wenceslao; Pérez González, Ana; Estimation of the marginal location under a partially linear model with missing responses; Elsevier Science; Computational Statistics and Data Analysis; 54; 2; 2-2010; 546-5640167-9473CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2009.09.028info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167947309003582info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:46:38Zoai:ri.conicet.gov.ar:11336/160246instacron: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 13:46:39.014CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimation of the marginal location under a partially linear model with missing responses
title Estimation of the marginal location under a partially linear model with missing responses
spellingShingle Estimation of the marginal location under a partially linear model with missing responses
Bianco, Ana Maria
Fisher--consistency
Kernel Weights
M-location Functionals
Missing at Random
Nonparametric Regression
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
title_short Estimation of the marginal location under a partially linear model with missing responses
title_full Estimation of the marginal location under a partially linear model with missing responses
title_fullStr Estimation of the marginal location under a partially linear model with missing responses
title_full_unstemmed Estimation of the marginal location under a partially linear model with missing responses
title_sort Estimation of the marginal location under a partially linear model with missing responses
dc.creator.none.fl_str_mv Bianco, Ana Maria
Boente Boente, Graciela Lina
González Manteiga, Wenceslao
Pérez González, Ana
author Bianco, Ana Maria
author_facet Bianco, Ana Maria
Boente Boente, Graciela Lina
González Manteiga, Wenceslao
Pérez González, Ana
author_role author
author2 Boente Boente, Graciela Lina
González Manteiga, Wenceslao
Pérez González, Ana
author2_role author
author
author
dc.subject.none.fl_str_mv Fisher--consistency
Kernel Weights
M-location Functionals
Missing at Random
Nonparametric Regression
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
topic Fisher--consistency
Kernel Weights
M-location Functionals
Missing at Random
Nonparametric Regression
Robust Estimation
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
description In this paper, we consider a semiparametric partially linear regression model where there are missing data in the response. We propose robust Fisher-consistent estimators for the regression parameter, for the regression function and for the marginal location parameter of the response variable. A robust cross-validation method is briefly discussed, although, from our numerical results, the marginal estimators seem not to be sensitive to the bandwidth parameter. Finally, a Monte Carlo study is carried out to compare the performance of the robust proposed estimators among themselves and also with the classical ones, for normal and contaminated samples, under different missing data models. An example based on a real data set is also discussed.
publishDate 2010
dc.date.none.fl_str_mv 2010-02
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/11336/160246
Bianco, Ana Maria; Boente Boente, Graciela Lina; González Manteiga, Wenceslao; Pérez González, Ana; Estimation of the marginal location under a partially linear model with missing responses; Elsevier Science; Computational Statistics and Data Analysis; 54; 2; 2-2010; 546-564
0167-9473
CONICET Digital
CONICET
url http://hdl.handle.net/11336/160246
identifier_str_mv Bianco, Ana Maria; Boente Boente, Graciela Lina; González Manteiga, Wenceslao; Pérez González, Ana; Estimation of the marginal location under a partially linear model with missing responses; Elsevier Science; Computational Statistics and Data Analysis; 54; 2; 2-2010; 546-564
0167-9473
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.csda.2009.09.028
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167947309003582
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
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 15,811543

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