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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/14925
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:43:43Zoai: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:34982025-09-29 09:43:43.728CONICET 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 |
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1844613376260440064 |
score |
13.070432 |