A new projection estimate for multivariate location with minimax bias

Autores
Adrover, Jorge Gabriel; Yohai, Victor Jaime
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The maximum asymptotic bias of an estimator is a global robustness measure of its performance. The projection median estimator for multivariate location shows a remarkable behavior regarding asymptotic bias. In this paper we consider a modification of the projection median estimator which renders an estimate with better bias performance for point mass contaminations (the worst situation for the projection median estimator). Moreover, it achieves the lowest bound for an equivariant estimate for point mass contaminations.
Fil: Adrover, Jorge Gabriel. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Projection Estimates
Maximum Bias
Robust Estimates
Multivariate Location
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:11336/16528
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/16528
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A new projection estimate for multivariate location with minimax biasAdrover, Jorge GabrielYohai, Victor JaimeProjection EstimatesMaximum BiasRobust EstimatesMultivariate Locationhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1The maximum asymptotic bias of an estimator is a global robustness measure of its performance. The projection median estimator for multivariate location shows a remarkable behavior regarding asymptotic bias. In this paper we consider a modification of the projection median estimator which renders an estimate with better bias performance for point mass contaminations (the worst situation for the projection median estimator). Moreover, it achieves the lowest bound for an equivariant estimate for point mass contaminations.Fil: Adrover, Jorge Gabriel. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaFil: Yohai, Victor Jaime. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Inc2010-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/16528Adrover, Jorge Gabriel; Yohai, Victor Jaime; A new projection estimate for multivariate location with minimax bias; Elsevier Inc; Journal Of Multivariate Analysis; 101; 6; 7-2010; 1400-14110047-259Xenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2009.12.007info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X09002267info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:18:06Zoai:ri.conicet.gov.ar:11336/16528instacron: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:34982025-10-15 15:18:06.691CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A new projection estimate for multivariate location with minimax bias
title A new projection estimate for multivariate location with minimax bias
spellingShingle A new projection estimate for multivariate location with minimax bias
Adrover, Jorge Gabriel
Projection Estimates
Maximum Bias
Robust Estimates
Multivariate Location
title_short A new projection estimate for multivariate location with minimax bias
title_full A new projection estimate for multivariate location with minimax bias
title_fullStr A new projection estimate for multivariate location with minimax bias
title_full_unstemmed A new projection estimate for multivariate location with minimax bias
title_sort A new projection estimate for multivariate location with minimax bias
dc.creator.none.fl_str_mv Adrover, Jorge Gabriel
Yohai, Victor Jaime
author Adrover, Jorge Gabriel
author_facet Adrover, Jorge Gabriel
Yohai, Victor Jaime
author_role author
author2 Yohai, Victor Jaime
author2_role author
dc.subject.none.fl_str_mv Projection Estimates
Maximum Bias
Robust Estimates
Multivariate Location
topic Projection Estimates
Maximum Bias
Robust Estimates
Multivariate Location
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The maximum asymptotic bias of an estimator is a global robustness measure of its performance. The projection median estimator for multivariate location shows a remarkable behavior regarding asymptotic bias. In this paper we consider a modification of the projection median estimator which renders an estimate with better bias performance for point mass contaminations (the worst situation for the projection median estimator). Moreover, it achieves the lowest bound for an equivariant estimate for point mass contaminations.
Fil: Adrover, Jorge Gabriel. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description The maximum asymptotic bias of an estimator is a global robustness measure of its performance. The projection median estimator for multivariate location shows a remarkable behavior regarding asymptotic bias. In this paper we consider a modification of the projection median estimator which renders an estimate with better bias performance for point mass contaminations (the worst situation for the projection median estimator). Moreover, it achieves the lowest bound for an equivariant estimate for point mass contaminations.
publishDate 2010
dc.date.none.fl_str_mv 2010-07
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/11336/16528
Adrover, Jorge Gabriel; Yohai, Victor Jaime; A new projection estimate for multivariate location with minimax bias; Elsevier Inc; Journal Of Multivariate Analysis; 101; 6; 7-2010; 1400-1411
0047-259X
url http://hdl.handle.net/11336/16528
identifier_str_mv Adrover, Jorge Gabriel; Yohai, Victor Jaime; A new projection estimate for multivariate location with minimax bias; Elsevier Inc; Journal Of Multivariate Analysis; 101; 6; 7-2010; 1400-1411
0047-259X
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.2009.12.007
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X09002267
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 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
_version_ 1846083329896480768
score 13.22299

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