Robust functional principal components: A projection-pursuit approach

Autores
Bali, Juan Lucas; Boente Boente, Graciela Lina; Tyler, David E.; Wang, Jane Ling
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection-pursuit with different smoothing methods. Consistency of the estimators are shown under mild assumptions. The performance of the classical and robust procedures are compared in a simulation study under different contamination schemes.
Fil: Bali, Juan Lucas. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Tyler, David E.. University Of California At Davis; Estados Unidos
Fil: Wang, Jane Ling. University Of California At Davis; Estados Unidos
Materia
FISHER-CONSISTENCY
FUNCTIONAL DATA
METHOD OF SIEVES
PENALIZATION
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:11336/14925
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/14925
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust functional principal components: A projection-pursuit approachBali, Juan LucasBoente Boente, Graciela LinaTyler, David E.Wang, Jane LingFISHER-CONSISTENCYFUNCTIONAL DATAMETHOD OF SIEVESPENALIZATIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection-pursuit with different smoothing methods. Consistency of the estimators are shown under mild assumptions. The performance of the classical and robust procedures are compared in a simulation study under different contamination schemes.Fil: Bali, Juan Lucas. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Boente Boente, Graciela Lina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Tyler, David E.. University Of California At Davis; Estados UnidosFil: Wang, Jane Ling. University Of California At Davis; Estados UnidosInst Mathematical Statistics2011-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14925Bali, Juan Lucas; Boente Boente, Graciela Lina; Tyler, David E.; Wang, Jane Ling; Robust functional principal components: A projection-pursuit approach; Inst Mathematical Statistics; Annals Of Statistics, The; 39; 6; 12-2011; 2852-28820090-5364enginfo:eu-repo/semantics/altIdentifier/url/http://projecteuclid.org/euclid.aos/1327413771info:eu-repo/semantics/altIdentifier/doi/10.1214/11-AOS923info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:12:25Zoai:ri.conicet.gov.ar:11336/14925instacron: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:34982025-10-22 11:12:25.512CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust functional principal components: A projection-pursuit approach
title Robust functional principal components: A projection-pursuit approach
spellingShingle Robust functional principal components: A projection-pursuit approach
Bali, Juan Lucas
FISHER-CONSISTENCY
FUNCTIONAL DATA
METHOD OF SIEVES
PENALIZATION
title_short Robust functional principal components: A projection-pursuit approach
title_full Robust functional principal components: A projection-pursuit approach
title_fullStr Robust functional principal components: A projection-pursuit approach
title_full_unstemmed Robust functional principal components: A projection-pursuit approach
title_sort Robust functional principal components: A projection-pursuit approach
dc.creator.none.fl_str_mv Bali, Juan Lucas
Boente Boente, Graciela Lina
Tyler, David E.
Wang, Jane Ling
author Bali, Juan Lucas
author_facet Bali, Juan Lucas
Boente Boente, Graciela Lina
Tyler, David E.
Wang, Jane Ling
author_role author
author2 Boente Boente, Graciela Lina
Tyler, David E.
Wang, Jane Ling
author2_role author
author
author
dc.subject.none.fl_str_mv FISHER-CONSISTENCY
FUNCTIONAL DATA
METHOD OF SIEVES
PENALIZATION
topic FISHER-CONSISTENCY
FUNCTIONAL DATA
METHOD OF SIEVES
PENALIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection-pursuit with different smoothing methods. Consistency of the estimators are shown under mild assumptions. The performance of the classical and robust procedures are compared in a simulation study under different contamination schemes.
Fil: Bali, Juan Lucas. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Tyler, David E.. University Of California At Davis; Estados Unidos
Fil: Wang, Jane Ling. University Of California At Davis; Estados Unidos
description In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection-pursuit with different smoothing methods. Consistency of the estimators are shown under mild assumptions. The performance of the classical and robust procedures are compared in a simulation study under different contamination schemes.
publishDate 2011
dc.date.none.fl_str_mv 2011-12
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/11336/14925
Bali, Juan Lucas; Boente Boente, Graciela Lina; Tyler, David E.; Wang, Jane Ling; Robust functional principal components: A projection-pursuit approach; Inst Mathematical Statistics; Annals Of Statistics, The; 39; 6; 12-2011; 2852-2882
0090-5364
url http://hdl.handle.net/11336/14925
identifier_str_mv Bali, Juan Lucas; Boente Boente, Graciela Lina; Tyler, David E.; Wang, Jane Ling; Robust functional principal components: A projection-pursuit approach; Inst Mathematical Statistics; Annals Of Statistics, The; 39; 6; 12-2011; 2852-2882
0090-5364
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://projecteuclid.org/euclid.aos/1327413771
info:eu-repo/semantics/altIdentifier/doi/10.1214/11-AOS923
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/pdf
dc.publisher.none.fl_str_mv Inst Mathematical Statistics
publisher.none.fl_str_mv Inst Mathematical Statistics
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_ 1846781516917506048
score 12.982451

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