General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study

Autores
Boente, G.; Pires, A.M.; Rodrigues, I.M.
Año de publicación
2006
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The common principal components (CPC) model for several groups of multivariate observations assumes equal principal axes but possibly different variances along these axes among the groups. Under a CPCs model, generalized projection-pursuit estimators are defined by using score functions on the dispersion measure considered. Their partial influence functions are obtained and asymptotic variances are derived from them. When the score function is taken equal to the logarithm, it is shown that, under a proportionality model, the eigenvector estimators are optimal in the sense of minimizing the asymptotic variance of the eigenvectors, for a given scale measure. © 2004 Elsevier Inc. All rights reserved.
Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fuente
J. Multivariate Anal. 2006;97(1):124-147
Materia
Asymptotic variances
Common principal components
Partial influence function
Projection-pursuit
Robust estimation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_0047259X_v97_n1_p124_Boente
Acceder
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oai_identifier_str paperaa:paper_0047259X_v97_n1_p124_Boente
network_acronym_str BDUBAFCEN
repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo studyBoente, G.Pires, A.M.Rodrigues, I.M.Asymptotic variancesCommon principal componentsPartial influence functionProjection-pursuitRobust estimationThe common principal components (CPC) model for several groups of multivariate observations assumes equal principal axes but possibly different variances along these axes among the groups. Under a CPCs model, generalized projection-pursuit estimators are defined by using score functions on the dispersion measure considered. Their partial influence functions are obtained and asymptotic variances are derived from them. When the score function is taken equal to the logarithm, it is shown that, under a proportionality model, the eigenvector estimators are optimal in the sense of minimizing the asymptotic variance of the eigenvectors, for a given scale measure. © 2004 Elsevier Inc. All rights reserved.Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.2006info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_0047259X_v97_n1_p124_BoenteJ. Multivariate Anal. 2006;97(1):124-147reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-10-16T09:30:20Zpaperaa:paper_0047259X_v97_n1_p124_BoenteInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-10-16 09:30:22.578Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
title General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
spellingShingle General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
Boente, G.
Asymptotic variances
Common principal components
Partial influence function
Projection-pursuit
Robust estimation
title_short General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
title_full General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
title_fullStr General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
title_full_unstemmed General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
title_sort General projection-pursuit estimators for the common principal components model: Influence functions and Monte Carlo study
dc.creator.none.fl_str_mv Boente, G.
Pires, A.M.
Rodrigues, I.M.
author Boente, G.
author_facet Boente, G.
Pires, A.M.
Rodrigues, I.M.
author_role author
author2 Pires, A.M.
Rodrigues, I.M.
author2_role author
author
dc.subject.none.fl_str_mv Asymptotic variances
Common principal components
Partial influence function
Projection-pursuit
Robust estimation
topic Asymptotic variances
Common principal components
Partial influence function
Projection-pursuit
Robust estimation
dc.description.none.fl_txt_mv The common principal components (CPC) model for several groups of multivariate observations assumes equal principal axes but possibly different variances along these axes among the groups. Under a CPCs model, generalized projection-pursuit estimators are defined by using score functions on the dispersion measure considered. Their partial influence functions are obtained and asymptotic variances are derived from them. When the score function is taken equal to the logarithm, it is shown that, under a proportionality model, the eigenvector estimators are optimal in the sense of minimizing the asymptotic variance of the eigenvectors, for a given scale measure. © 2004 Elsevier Inc. All rights reserved.
Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
description The common principal components (CPC) model for several groups of multivariate observations assumes equal principal axes but possibly different variances along these axes among the groups. Under a CPCs model, generalized projection-pursuit estimators are defined by using score functions on the dispersion measure considered. Their partial influence functions are obtained and asymptotic variances are derived from them. When the score function is taken equal to the logarithm, it is shown that, under a proportionality model, the eigenvector estimators are optimal in the sense of minimizing the asymptotic variance of the eigenvectors, for a given scale measure. © 2004 Elsevier Inc. All rights reserved.
publishDate 2006
dc.date.none.fl_str_mv 2006
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/20.500.12110/paper_0047259X_v97_n1_p124_Boente
url http://hdl.handle.net/20.500.12110/paper_0047259X_v97_n1_p124_Boente
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv J. Multivariate Anal. 2006;97(1):124-147
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 12.712165

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