Robust smoothed canonical correlation analysis for functional data

Autores
Boente Boente, Graciela Lina; Kudraszow, Nadia Laura
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper provides robust estimators for the first canonical correlation anddirections of random elements on Hilbert separable spaces by using robust association and scale measures combined with basis expansion and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent. The finitesample performance of our proposal is illustrated through a simulation study that showsthat, as expected, the robust method outperforms the existing classical procedure whenthe data are contaminated. A real data example is also presented.
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires; Argentina
Fil: Kudraszow, Nadia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata; Argentina
Materia
CANONICAL CORRELATION ANALYSIS
FUNCTIONAL DATA
ROBUST ESTIMATION
SMOOTHING TECHNIQUES
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:11336/143226
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/143226
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust smoothed canonical correlation analysis for functional dataBoente Boente, Graciela LinaKudraszow, Nadia LauraCANONICAL CORRELATION ANALYSISFUNCTIONAL DATAROBUST ESTIMATIONSMOOTHING TECHNIQUEShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1This paper provides robust estimators for the first canonical correlation anddirections of random elements on Hilbert separable spaces by using robust association and scale measures combined with basis expansion and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent. The finitesample performance of our proposal is illustrated through a simulation study that showsthat, as expected, the robust method outperforms the existing classical procedure whenthe data are contaminated. A real data example is also presented.Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires; ArgentinaFil: Kudraszow, Nadia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata; ArgentinaStatistica Sinica2021-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/143226Boente Boente, Graciela Lina; Kudraszow, Nadia Laura; Robust smoothed canonical correlation analysis for functional data; Statistica Sinica; Statistica Sinica; 32; 9-2021; 1-251017-0405CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www3.stat.sinica.edu.tw/statistica/J32N3/J32N305/J32N305.htmlinfo:eu-repo/semantics/altIdentifier/doi/10.5705/ss.202020.0084info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2011.10576info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2011.10576info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:37:06Zoai:ri.conicet.gov.ar:11336/143226instacron: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 10:37:06.871CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust smoothed canonical correlation analysis for functional data
title Robust smoothed canonical correlation analysis for functional data
spellingShingle Robust smoothed canonical correlation analysis for functional data
Boente Boente, Graciela Lina
CANONICAL CORRELATION ANALYSIS
FUNCTIONAL DATA
ROBUST ESTIMATION
SMOOTHING TECHNIQUES
title_short Robust smoothed canonical correlation analysis for functional data
title_full Robust smoothed canonical correlation analysis for functional data
title_fullStr Robust smoothed canonical correlation analysis for functional data
title_full_unstemmed Robust smoothed canonical correlation analysis for functional data
title_sort Robust smoothed canonical correlation analysis for functional data
dc.creator.none.fl_str_mv Boente Boente, Graciela Lina
Kudraszow, Nadia Laura
author Boente Boente, Graciela Lina
author_facet Boente Boente, Graciela Lina
Kudraszow, Nadia Laura
author_role author
author2 Kudraszow, Nadia Laura
author2_role author
dc.subject.none.fl_str_mv CANONICAL CORRELATION ANALYSIS
FUNCTIONAL DATA
ROBUST ESTIMATION
SMOOTHING TECHNIQUES
topic CANONICAL CORRELATION ANALYSIS
FUNCTIONAL DATA
ROBUST ESTIMATION
SMOOTHING TECHNIQUES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper provides robust estimators for the first canonical correlation anddirections of random elements on Hilbert separable spaces by using robust association and scale measures combined with basis expansion and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent. The finitesample performance of our proposal is illustrated through a simulation study that showsthat, as expected, the robust method outperforms the existing classical procedure whenthe data are contaminated. A real data example is also presented.
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires; Argentina
Fil: Kudraszow, Nadia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata; Argentina
description This paper provides robust estimators for the first canonical correlation anddirections of random elements on Hilbert separable spaces by using robust association and scale measures combined with basis expansion and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent. The finitesample performance of our proposal is illustrated through a simulation study that showsthat, as expected, the robust method outperforms the existing classical procedure whenthe data are contaminated. A real data example is also presented.
publishDate 2021
dc.date.none.fl_str_mv 2021-09
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/11336/143226
Boente Boente, Graciela Lina; Kudraszow, Nadia Laura; Robust smoothed canonical correlation analysis for functional data; Statistica Sinica; Statistica Sinica; 32; 9-2021; 1-25
1017-0405
CONICET Digital
CONICET
url http://hdl.handle.net/11336/143226
identifier_str_mv Boente Boente, Graciela Lina; Kudraszow, Nadia Laura; Robust smoothed canonical correlation analysis for functional data; Statistica Sinica; Statistica Sinica; 32; 9-2021; 1-25
1017-0405
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www3.stat.sinica.edu.tw/statistica/J32N3/J32N305/J32N305.html
info:eu-repo/semantics/altIdentifier/doi/10.5705/ss.202020.0084
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2011.10576
info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2011.10576
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
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Statistica Sinica
publisher.none.fl_str_mv Statistica Sinica
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_ 1844614391451877376
score 13.070432

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