Covariance reducing models: An alternative to spectral modelling of covariance matrices

Autores
Cook, R. Dennis; Forzani, Liliana Maria
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We introduce covariance reducing models for studying the sample covariance matrices of a random vector observed in different populations. The models are based on reducing the sample covariance matrices to an informational core that is sufficient to characterize the variance heterogeneity among the populations. They possess useful equivariance properties and provide a clear alternative to spectral models for covariance matrices.
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Materia
CENTRAL SUBSPACE
DIMENSION REDUCTION
ENVELOPES
GRASSMANN MANIFOLDS
REDUCING SUBSPACES
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:11336/84236
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/84236
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Covariance reducing models: An alternative to spectral modelling of covariance matricesCook, R. DennisForzani, Liliana MariaCENTRAL SUBSPACEDIMENSION REDUCTIONENVELOPESGRASSMANN MANIFOLDSREDUCING SUBSPACEShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We introduce covariance reducing models for studying the sample covariance matrices of a random vector observed in different populations. The models are based on reducing the sample covariance matrices to an informational core that is sufficient to characterize the variance heterogeneity among the populations. They possess useful equivariance properties and provide a clear alternative to spectral models for covariance matrices.Fil: Cook, R. Dennis. University of Minnesota; Estados UnidosFil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaOxford University Press2008-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/x-texapplication/pdfhttp://hdl.handle.net/11336/84236Cook, R. Dennis; Forzani, Liliana Maria; Covariance reducing models: An alternative to spectral modelling of covariance matrices; Oxford University Press; Biometrika; 95; 4; 12-2008; 799-8120006-3444CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1093/biomet/asn052info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/biomet/article-abstract/95/4/799/262858info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:20:20Zoai:ri.conicet.gov.ar:11336/84236instacron: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 10:20:21.219CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Covariance reducing models: An alternative to spectral modelling of covariance matrices
title Covariance reducing models: An alternative to spectral modelling of covariance matrices
spellingShingle Covariance reducing models: An alternative to spectral modelling of covariance matrices
Cook, R. Dennis
CENTRAL SUBSPACE
DIMENSION REDUCTION
ENVELOPES
GRASSMANN MANIFOLDS
REDUCING SUBSPACES
title_short Covariance reducing models: An alternative to spectral modelling of covariance matrices
title_full Covariance reducing models: An alternative to spectral modelling of covariance matrices
title_fullStr Covariance reducing models: An alternative to spectral modelling of covariance matrices
title_full_unstemmed Covariance reducing models: An alternative to spectral modelling of covariance matrices
title_sort Covariance reducing models: An alternative to spectral modelling of covariance matrices
dc.creator.none.fl_str_mv Cook, R. Dennis
Forzani, Liliana Maria
author Cook, R. Dennis
author_facet Cook, R. Dennis
Forzani, Liliana Maria
author_role author
author2 Forzani, Liliana Maria
author2_role author
dc.subject.none.fl_str_mv CENTRAL SUBSPACE
DIMENSION REDUCTION
ENVELOPES
GRASSMANN MANIFOLDS
REDUCING SUBSPACES
topic CENTRAL SUBSPACE
DIMENSION REDUCTION
ENVELOPES
GRASSMANN MANIFOLDS
REDUCING SUBSPACES
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 introduce covariance reducing models for studying the sample covariance matrices of a random vector observed in different populations. The models are based on reducing the sample covariance matrices to an informational core that is sufficient to characterize the variance heterogeneity among the populations. They possess useful equivariance properties and provide a clear alternative to spectral models for covariance matrices.
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
description We introduce covariance reducing models for studying the sample covariance matrices of a random vector observed in different populations. The models are based on reducing the sample covariance matrices to an informational core that is sufficient to characterize the variance heterogeneity among the populations. They possess useful equivariance properties and provide a clear alternative to spectral models for covariance matrices.
publishDate 2008
dc.date.none.fl_str_mv 2008-12
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/11336/84236
Cook, R. Dennis; Forzani, Liliana Maria; Covariance reducing models: An alternative to spectral modelling of covariance matrices; Oxford University Press; Biometrika; 95; 4; 12-2008; 799-812
0006-3444
CONICET Digital
CONICET
url http://hdl.handle.net/11336/84236
identifier_str_mv Cook, R. Dennis; Forzani, Liliana Maria; Covariance reducing models: An alternative to spectral modelling of covariance matrices; Oxford University Press; Biometrika; 95; 4; 12-2008; 799-812
0006-3444
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1093/biomet/asn052
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/biomet/article-abstract/95/4/799/262858
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/x-tex
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
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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