Estimates of MM type for the multivariate linear model

Autores
Kudraszow, Nadia Laura; Maronna, Ricardo A.
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-estimation (Yohai 1987, [24]), we estimate the regression coefficients and the covariance matrix of the errors simultaneously. These estimates have both a high breakdown point and high asymptotic efficiency under Gaussian errors. We prove consistency and asymptotic normality assuming errors with an elliptical distribution. We describe an iterative algorithm for the numerical calculation of these estimates. The advantages of the proposed estimates over their competitors are demonstrated through both simulated and real data.
Fil: Kudraszow, Nadia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata; Argentina
Fil: Maronna, Ricardo A.. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Universidad Nacional de La Plata; Argentina
Materia
ROBUST METHODS
MM-ESTIMATE
MULTIVARIATE LINEAR MODEL
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:11336/95041
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/95041
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Estimates of MM type for the multivariate linear modelKudraszow, Nadia LauraMaronna, Ricardo A.ROBUST METHODSMM-ESTIMATEMULTIVARIATE LINEAR MODELhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-estimation (Yohai 1987, [24]), we estimate the regression coefficients and the covariance matrix of the errors simultaneously. These estimates have both a high breakdown point and high asymptotic efficiency under Gaussian errors. We prove consistency and asymptotic normality assuming errors with an elliptical distribution. We describe an iterative algorithm for the numerical calculation of these estimates. The advantages of the proposed estimates over their competitors are demonstrated through both simulated and real data.Fil: Kudraszow, Nadia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Maronna, Ricardo A.. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Universidad Nacional de La Plata; ArgentinaElsevier Inc2011-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/95041Kudraszow, Nadia Laura; Maronna, Ricardo A.; Estimates of MM type for the multivariate linear model; Elsevier Inc; Journal Of Multivariate Analysis; 102; 9; 10-2011; 1280-12920047-259XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2011.04.011info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0047259X11000674info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:21:50Zoai:ri.conicet.gov.ar:11336/95041instacron: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 10:21:50.714CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimates of MM type for the multivariate linear model
title Estimates of MM type for the multivariate linear model
spellingShingle Estimates of MM type for the multivariate linear model
Kudraszow, Nadia Laura
ROBUST METHODS
MM-ESTIMATE
MULTIVARIATE LINEAR MODEL
title_short Estimates of MM type for the multivariate linear model
title_full Estimates of MM type for the multivariate linear model
title_fullStr Estimates of MM type for the multivariate linear model
title_full_unstemmed Estimates of MM type for the multivariate linear model
title_sort Estimates of MM type for the multivariate linear model
dc.creator.none.fl_str_mv Kudraszow, Nadia Laura
Maronna, Ricardo A.
author Kudraszow, Nadia Laura
author_facet Kudraszow, Nadia Laura
Maronna, Ricardo A.
author_role author
author2 Maronna, Ricardo A.
author2_role author
dc.subject.none.fl_str_mv ROBUST METHODS
MM-ESTIMATE
MULTIVARIATE LINEAR MODEL
topic ROBUST METHODS
MM-ESTIMATE
MULTIVARIATE LINEAR MODEL
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-estimation (Yohai 1987, [24]), we estimate the regression coefficients and the covariance matrix of the errors simultaneously. These estimates have both a high breakdown point and high asymptotic efficiency under Gaussian errors. We prove consistency and asymptotic normality assuming errors with an elliptical distribution. We describe an iterative algorithm for the numerical calculation of these estimates. The advantages of the proposed estimates over their competitors are demonstrated through both simulated and real data.
Fil: Kudraszow, Nadia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata; Argentina
Fil: Maronna, Ricardo A.. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Universidad Nacional de La Plata; Argentina
description We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-estimation (Yohai 1987, [24]), we estimate the regression coefficients and the covariance matrix of the errors simultaneously. These estimates have both a high breakdown point and high asymptotic efficiency under Gaussian errors. We prove consistency and asymptotic normality assuming errors with an elliptical distribution. We describe an iterative algorithm for the numerical calculation of these estimates. The advantages of the proposed estimates over their competitors are demonstrated through both simulated and real data.
publishDate 2011
dc.date.none.fl_str_mv 2011-10
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/11336/95041
Kudraszow, Nadia Laura; Maronna, Ricardo A.; Estimates of MM type for the multivariate linear model; Elsevier Inc; Journal Of Multivariate Analysis; 102; 9; 10-2011; 1280-1292
0047-259X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/95041
identifier_str_mv Kudraszow, Nadia Laura; Maronna, Ricardo A.; Estimates of MM type for the multivariate linear model; Elsevier Inc; Journal Of Multivariate Analysis; 102; 9; 10-2011; 1280-1292
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.2011.04.011
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0047259X11000674
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
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
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
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score 13.070432

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