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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/15932
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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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13.070432 |