Nonparametric regression based on discretely sampled curves

Forzani, Liliana - Fraiman, Ricardo - Llop, Pamela

Resumen:

In the context of nonparametric regression, we study conditions under which the consistency (and rates of convergence) of estimators built from discretely sampled curves can be derived from the consistency of estimators based on the unobserved whole trajectories. As a consequence, we derive asymptotic results for most of the regularization techniques used in functional data analysis, including smoothing and basis representation.


Detalles Bibliográficos
2020
Nonparametric regression
Functional data
Discrete curves
Inglés
Universidad de la República
COLIBRI
https://hdl.handle.net/20.500.12008/33813
Acceso abierto
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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author Forzani, Liliana
author2 Fraiman, Ricardo
Llop, Pamela
author2_role author
author
author_facet Forzani, Liliana
Fraiman, Ricardo
Llop, Pamela
author_role author
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collection COLIBRI
dc.contributor.filiacion.none.fl_str_mv Forzani Liliana, Universidad Nacional del Litoral (Argentina)
Fraiman Ricardo, Universidad de la República (Uruguay). Facultad de Ciencias. Centro de Matemática.
Llop Pamela, Universidad Nacional del Litoral (Argentina)
dc.creator.none.fl_str_mv Forzani, Liliana
Fraiman, Ricardo
Llop, Pamela
dc.date.accessioned.none.fl_str_mv 2022-09-13T13:16:42Z
dc.date.available.none.fl_str_mv 2022-09-13T13:16:42Z
dc.date.issued.none.fl_str_mv 2020
dc.description.abstract.none.fl_txt_mv In the context of nonparametric regression, we study conditions under which the consistency (and rates of convergence) of estimators built from discretely sampled curves can be derived from the consistency of estimators based on the unobserved whole trajectories. As a consequence, we derive asymptotic results for most of the regularization techniques used in functional data analysis, including smoothing and basis representation.
dc.format.extent.es.fl_str_mv 26 h
dc.format.mimetype.es.fl_str_mv application/pdf
dc.identifier.citation.es.fl_str_mv Forzani, L, Fraiman, R y Llop, P. "Nonparametric regression based on discretely sampled curves". REVSTAT – Statistical Journal. [en línea] 2020, 18(1): 1-26. 26 h.
dc.identifier.issn.none.fl_str_mv 2183-0371
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12008/33813
dc.language.iso.none.fl_str_mv en
eng
dc.publisher.es.fl_str_mv Instituto Nacional Estatística
dc.relation.ispartof.es.fl_str_mv REVSTAT – Statistical Journal, 2020, 18(1): 1-26
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
dc.subject.es.fl_str_mv Nonparametric regression
Functional data
Discrete curves
dc.title.none.fl_str_mv Nonparametric regression based on discretely sampled curves
dc.type.es.fl_str_mv Artículo
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
description In the context of nonparametric regression, we study conditions under which the consistency (and rates of convergence) of estimators built from discretely sampled curves can be derived from the consistency of estimators based on the unobserved whole trajectories. As a consequence, we derive asymptotic results for most of the regularization techniques used in functional data analysis, including smoothing and basis representation.
eu_rights_str_mv openAccess
format article
id COLIBRI_adc055f51f51d42376694081baf3cd92
identifier_str_mv Forzani, L, Fraiman, R y Llop, P. "Nonparametric regression based on discretely sampled curves". REVSTAT – Statistical Journal. [en línea] 2020, 18(1): 1-26. 26 h.
2183-0371
instacron_str Universidad de la República
institution Universidad de la República
instname_str Universidad de la República
language eng
language_invalid_str_mv en
network_acronym_str COLIBRI
network_name_str COLIBRI
oai_identifier_str oai:colibri.udelar.edu.uy:20.500.12008/33813
publishDate 2020
reponame_str COLIBRI
repository.mail.fl_str_mv mabel.seroubian@seciu.edu.uy
repository.name.fl_str_mv COLIBRI - Universidad de la República
repository_id_str 4771
rights_invalid_str_mv Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
spelling Forzani Liliana, Universidad Nacional del Litoral (Argentina)Fraiman Ricardo, Universidad de la República (Uruguay). Facultad de Ciencias. Centro de Matemática.Llop Pamela, Universidad Nacional del Litoral (Argentina)2022-09-13T13:16:42Z2022-09-13T13:16:42Z2020Forzani, L, Fraiman, R y Llop, P. "Nonparametric regression based on discretely sampled curves". REVSTAT – Statistical Journal. [en línea] 2020, 18(1): 1-26. 26 h.2183-0371https://hdl.handle.net/20.500.12008/33813In the context of nonparametric regression, we study conditions under which the consistency (and rates of convergence) of estimators built from discretely sampled curves can be derived from the consistency of estimators based on the unobserved whole trajectories. As a consequence, we derive asymptotic results for most of the regularization techniques used in functional data analysis, including smoothing and basis representation.Submitted by Faget Cecilia (lfaget@fcien.edu.uy) on 2022-09-12T18:18:50Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) FRAnon2020.pdf: 378159 bytes, checksum: cd80bd3e91fb23809bb031b2746d1e3d (MD5)Approved for entry into archive by Faget Cecilia (lfaget@fcien.edu.uy) on 2022-09-13T13:10:44Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) FRAnon2020.pdf: 378159 bytes, checksum: cd80bd3e91fb23809bb031b2746d1e3d (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2022-09-13T13:16:42Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) FRAnon2020.pdf: 378159 bytes, checksum: cd80bd3e91fb23809bb031b2746d1e3d (MD5) Previous issue date: 202026 happlication/pdfenengInstituto Nacional EstatísticaREVSTAT – Statistical Journal, 2020, 18(1): 1-26Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Nonparametric regressionFunctional dataDiscrete curvesNonparametric regression based on discretely sampled curvesArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaForzani, LilianaFraiman, RicardoLlop, PamelaLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/33813/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/33813/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse
spellingShingle Nonparametric regression based on discretely sampled curves
Forzani, Liliana
Nonparametric regression
Functional data
Discrete curves
status_str publishedVersion
title Nonparametric regression based on discretely sampled curves
title_full Nonparametric regression based on discretely sampled curves
title_fullStr Nonparametric regression based on discretely sampled curves
title_full_unstemmed Nonparametric regression based on discretely sampled curves
title_short Nonparametric regression based on discretely sampled curves
title_sort Nonparametric regression based on discretely sampled curves
topic Nonparametric regression
Functional data
Discrete curves
url https://hdl.handle.net/20.500.12008/33813

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