Influence function of projection-pursuit principal components for functional data

Autores
Bali, Juan Lucas; Boente Boente, Graciela Lina
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components analysis using projection-pursuit techniques. This procedure was generalized to the functional setting by Bali et al. (2011), where also different penalized estimators were defined to provide smooth functional robust principal component estimators. This paper completes their study by deriving the influence function of the functional related to the principal direction estimators and their size. As is well known, the influence function is a measure of robustness which can also be used for diagnostic purposes. In this sense, the obtained results can be helpful for detecting influential observations for the principal directions.
Fil: Bali, Juan Lucas. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Elliptical Distribution
Fisher-Consistency
Functional Principal Component
Influence Function
Robust Estimation
Smoothing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/18939
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/18939
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Influence function of projection-pursuit principal components for functional dataBali, Juan LucasBoente Boente, Graciela LinaElliptical DistributionFisher-ConsistencyFunctional Principal ComponentInfluence FunctionRobust EstimationSmoothinghttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components analysis using projection-pursuit techniques. This procedure was generalized to the functional setting by Bali et al. (2011), where also different penalized estimators were defined to provide smooth functional robust principal component estimators. This paper completes their study by deriving the influence function of the functional related to the principal direction estimators and their size. As is well known, the influence function is a measure of robustness which can also be used for diagnostic purposes. In this sense, the obtained results can be helpful for detecting influential observations for the principal directions.Fil: Bali, Juan Lucas. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Inc2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/18939Bali, Juan Lucas; Boente Boente, Graciela Lina; Influence function of projection-pursuit principal components for functional data; Elsevier Inc; Journal Of Multivariate Analysis; 133; 1-2015; 173-1990047-259XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2014.09.004info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X14002012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:11:11Zoai:ri.conicet.gov.ar:11336/18939instacron: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 10:11:12.086CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Influence function of projection-pursuit principal components for functional data
title Influence function of projection-pursuit principal components for functional data
spellingShingle Influence function of projection-pursuit principal components for functional data
Bali, Juan Lucas
Elliptical Distribution
Fisher-Consistency
Functional Principal Component
Influence Function
Robust Estimation
Smoothing
title_short Influence function of projection-pursuit principal components for functional data
title_full Influence function of projection-pursuit principal components for functional data
title_fullStr Influence function of projection-pursuit principal components for functional data
title_full_unstemmed Influence function of projection-pursuit principal components for functional data
title_sort Influence function of projection-pursuit principal components for functional data
dc.creator.none.fl_str_mv Bali, Juan Lucas
Boente Boente, Graciela Lina
author Bali, Juan Lucas
author_facet Bali, Juan Lucas
Boente Boente, Graciela Lina
author_role author
author2 Boente Boente, Graciela Lina
author2_role author
dc.subject.none.fl_str_mv Elliptical Distribution
Fisher-Consistency
Functional Principal Component
Influence Function
Robust Estimation
Smoothing
topic Elliptical Distribution
Fisher-Consistency
Functional Principal Component
Influence Function
Robust Estimation
Smoothing
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 the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components analysis using projection-pursuit techniques. This procedure was generalized to the functional setting by Bali et al. (2011), where also different penalized estimators were defined to provide smooth functional robust principal component estimators. This paper completes their study by deriving the influence function of the functional related to the principal direction estimators and their size. As is well known, the influence function is a measure of robustness which can also be used for diagnostic purposes. In this sense, the obtained results can be helpful for detecting influential observations for the principal directions.
Fil: Bali, Juan Lucas. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components analysis using projection-pursuit techniques. This procedure was generalized to the functional setting by Bali et al. (2011), where also different penalized estimators were defined to provide smooth functional robust principal component estimators. This paper completes their study by deriving the influence function of the functional related to the principal direction estimators and their size. As is well known, the influence function is a measure of robustness which can also be used for diagnostic purposes. In this sense, the obtained results can be helpful for detecting influential observations for the principal directions.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/18939
Bali, Juan Lucas; Boente Boente, Graciela Lina; Influence function of projection-pursuit principal components for functional data; Elsevier Inc; Journal Of Multivariate Analysis; 133; 1-2015; 173-199
0047-259X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/18939
identifier_str_mv Bali, Juan Lucas; Boente Boente, Graciela Lina; Influence function of projection-pursuit principal components for functional data; Elsevier Inc; Journal Of Multivariate Analysis; 133; 1-2015; 173-199
0047-259X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2014.09.004
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X14002012
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 Inc
publisher.none.fl_str_mv Elsevier Inc
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_ 1844614008536039424
score 13.070432

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