A note on Smoothed Functional Inverse Regression

Autores
Forzani, Liliana Maria; Cook, R. Dennis
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Estimation in the context of functional data analysis is almost always non-parametric, since the object to be estimated lives in an infinite dimensional space. That is the case for the functional linear model with a real response and a process as covariables. In a recent paper Ferré and Yao state that the estimation of the Effective Dimension Reduction (EDR) subspace via SIR has parametric order. We show that a strong condition is needed for their statement to be true.
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
Materia
Dimension Reduction
Functional Data Analysis
Inverse Regression
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:11336/84268
Acceder
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spelling A note on Smoothed Functional Inverse RegressionForzani, Liliana MariaCook, R. DennisDimension ReductionFunctional Data AnalysisInverse Regressionhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Estimation in the context of functional data analysis is almost always non-parametric, since the object to be estimated lives in an infinite dimensional space. That is the case for the functional linear model with a real response and a process as covariables. In a recent paper Ferré and Yao state that the estimation of the Effective Dimension Reduction (EDR) subspace via SIR has parametric order. We show that a strong condition is needed for their statement to be true.Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaFil: Cook, R. Dennis. University of Minnesota; Estados UnidosStatistica Sinica2007-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/84268Forzani, Liliana Maria; Cook, R. Dennis; A note on Smoothed Functional Inverse Regression; Statistica Sinica; Statistica Sinica; 17; 4; 12-2007; 1677-16811017-0405CONICET DigitalCONICETenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:55:07Zoai:ri.conicet.gov.ar:11336/84268instacron: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 09:55:07.468CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A note on Smoothed Functional Inverse Regression
title A note on Smoothed Functional Inverse Regression
spellingShingle A note on Smoothed Functional Inverse Regression
Forzani, Liliana Maria
Dimension Reduction
Functional Data Analysis
Inverse Regression
title_short A note on Smoothed Functional Inverse Regression
title_full A note on Smoothed Functional Inverse Regression
title_fullStr A note on Smoothed Functional Inverse Regression
title_full_unstemmed A note on Smoothed Functional Inverse Regression
title_sort A note on Smoothed Functional Inverse Regression
dc.creator.none.fl_str_mv Forzani, Liliana Maria
Cook, R. Dennis
author Forzani, Liliana Maria
author_facet Forzani, Liliana Maria
Cook, R. Dennis
author_role author
author2 Cook, R. Dennis
author2_role author
dc.subject.none.fl_str_mv Dimension Reduction
Functional Data Analysis
Inverse Regression
topic Dimension Reduction
Functional Data Analysis
Inverse Regression
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Estimation in the context of functional data analysis is almost always non-parametric, since the object to be estimated lives in an infinite dimensional space. That is the case for the functional linear model with a real response and a process as covariables. In a recent paper Ferré and Yao state that the estimation of the Effective Dimension Reduction (EDR) subspace via SIR has parametric order. We show that a strong condition is needed for their statement to be true.
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
description Estimation in the context of functional data analysis is almost always non-parametric, since the object to be estimated lives in an infinite dimensional space. That is the case for the functional linear model with a real response and a process as covariables. In a recent paper Ferré and Yao state that the estimation of the Effective Dimension Reduction (EDR) subspace via SIR has parametric order. We show that a strong condition is needed for their statement to be true.
publishDate 2007
dc.date.none.fl_str_mv 2007-12
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/11336/84268
Forzani, Liliana Maria; Cook, R. Dennis; A note on Smoothed Functional Inverse Regression; Statistica Sinica; Statistica Sinica; 17; 4; 12-2007; 1677-1681
1017-0405
CONICET Digital
CONICET
url http://hdl.handle.net/11336/84268
identifier_str_mv Forzani, Liliana Maria; Cook, R. Dennis; A note on Smoothed Functional Inverse Regression; Statistica Sinica; Statistica Sinica; 17; 4; 12-2007; 1677-1681
1017-0405
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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 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
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score 13.070432

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