Robust minimum information loss estimation

Autores
Lind, John C.; Wiens, Douglas P.; Yohai, Victor Jaime
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample–typically of covariance or cross-spectrum matrices–with the subsample chosen to minimize a function of its average. In one case this function is the Kullback–Leibler discrimination information loss incurred when the subsample is summarized by its average; in the other it is the determinant, subject to a certain side condition. For each, the authors give an efficient computing algorithm, and show that the estimator has, asymptotically, the maximum possible breakdown point. The main motivation is the need for efficient and robust estimation of cross-spectrum matrices, and they present a case study in which the data points originate as multichannel electroencephalogram recordings but are then summarized by the corresponding sample cross-spectrum matrices.
Fil: Lind, John C.. Alberta Hospital Edmonton; Canadá
Fil: Wiens, Douglas P.. University of Alberta; Canadá
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Breakdown
Covariance Cross-Spectrum Matrix
Electroencephalogram Recording
Minimum Covariance Determinant
Trimmed Minimum Information Loss Estimate
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:11336/15932

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spelling Robust minimum information loss estimationLind, John C.Wiens, Douglas P.Yohai, Victor JaimeBreakdownCovariance Cross-Spectrum MatrixElectroencephalogram RecordingMinimum Covariance DeterminantTrimmed Minimum Information Loss Estimatehttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample–typically of covariance or cross-spectrum matrices–with the subsample chosen to minimize a function of its average. In one case this function is the Kullback–Leibler discrimination information loss incurred when the subsample is summarized by its average; in the other it is the determinant, subject to a certain side condition. For each, the authors give an efficient computing algorithm, and show that the estimator has, asymptotically, the maximum possible breakdown point. The main motivation is the need for efficient and robust estimation of cross-spectrum matrices, and they present a case study in which the data points originate as multichannel electroencephalogram recordings but are then summarized by the corresponding sample cross-spectrum matrices.Fil: Lind, John C.. Alberta Hospital Edmonton; CanadáFil: Wiens, Douglas P.. University of Alberta; CanadáFil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2013-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15932Lind, John C.; Wiens, Douglas P.; Yohai, Victor Jaime; Robust minimum information loss estimation; Elsevier Science; Computational Statistics And Data Analysis; 65; 9-2013; 98-1120167-9473enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2012.06.011info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167947312002526info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:22Zoai:ri.conicet.gov.ar:11336/15932instacron: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 09:32:22.385CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust minimum information loss estimation
title Robust minimum information loss estimation
spellingShingle Robust minimum information loss estimation
Lind, John C.
Breakdown
Covariance Cross-Spectrum Matrix
Electroencephalogram Recording
Minimum Covariance Determinant
Trimmed Minimum Information Loss Estimate
title_short Robust minimum information loss estimation
title_full Robust minimum information loss estimation
title_fullStr Robust minimum information loss estimation
title_full_unstemmed Robust minimum information loss estimation
title_sort Robust minimum information loss estimation
dc.creator.none.fl_str_mv Lind, John C.
Wiens, Douglas P.
Yohai, Victor Jaime
author Lind, John C.
author_facet Lind, John C.
Wiens, Douglas P.
Yohai, Victor Jaime
author_role author
author2 Wiens, Douglas P.
Yohai, Victor Jaime
author2_role author
author
dc.subject.none.fl_str_mv Breakdown
Covariance Cross-Spectrum Matrix
Electroencephalogram Recording
Minimum Covariance Determinant
Trimmed Minimum Information Loss Estimate
topic Breakdown
Covariance Cross-Spectrum Matrix
Electroencephalogram Recording
Minimum Covariance Determinant
Trimmed Minimum Information Loss Estimate
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample–typically of covariance or cross-spectrum matrices–with the subsample chosen to minimize a function of its average. In one case this function is the Kullback–Leibler discrimination information loss incurred when the subsample is summarized by its average; in the other it is the determinant, subject to a certain side condition. For each, the authors give an efficient computing algorithm, and show that the estimator has, asymptotically, the maximum possible breakdown point. The main motivation is the need for efficient and robust estimation of cross-spectrum matrices, and they present a case study in which the data points originate as multichannel electroencephalogram recordings but are then summarized by the corresponding sample cross-spectrum matrices.
Fil: Lind, John C.. Alberta Hospital Edmonton; Canadá
Fil: Wiens, Douglas P.. University of Alberta; Canadá
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample–typically of covariance or cross-spectrum matrices–with the subsample chosen to minimize a function of its average. In one case this function is the Kullback–Leibler discrimination information loss incurred when the subsample is summarized by its average; in the other it is the determinant, subject to a certain side condition. For each, the authors give an efficient computing algorithm, and show that the estimator has, asymptotically, the maximum possible breakdown point. The main motivation is the need for efficient and robust estimation of cross-spectrum matrices, and they present a case study in which the data points originate as multichannel electroencephalogram recordings but are then summarized by the corresponding sample cross-spectrum matrices.
publishDate 2013
dc.date.none.fl_str_mv 2013-09
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/11336/15932
Lind, John C.; Wiens, Douglas P.; Yohai, Victor Jaime; Robust minimum information loss estimation; Elsevier Science; Computational Statistics And Data Analysis; 65; 9-2013; 98-112
0167-9473
url http://hdl.handle.net/11336/15932
identifier_str_mv Lind, John C.; Wiens, Douglas P.; Yohai, Victor Jaime; Robust minimum information loss estimation; Elsevier Science; Computational Statistics And Data Analysis; 65; 9-2013; 98-112
0167-9473
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.2012.06.011
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167947312002526
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 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
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