Robust functional principal components: A projection-pursuit approach

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 robu...

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Detalles Bibliográficos
Autores principales: Bali, J.L., Boente, G., Tyler, D.E., Wang, J.-L.
Formato: JOUR
Materias:
Fisher-consistency
Functional data
Method of sieves
Outliers
Penalization
Principal component analysis
Robust estimation
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00905364_v39_n6_p2852_Bali
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_00905364_v39_n6_p2852_Bali
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spelling todo:paper_00905364_v39_n6_p2852_Bali2023-10-03T14:54:43Z Robust functional principal components: A projection-pursuit approach Bali, J.L. Boente, G. Tyler, D.E. Wang, J.-L. Fisher-consistency Functional data Method of sieves Outliers Penalization Principal component analysis Robust estimation 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. © Institute of Mathematical Statistics, 2011. Fil:Bali, J.L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00905364_v39_n6_p2852_Bali
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Fisher-consistency
Functional data
Method of sieves
Outliers
Penalization
Principal component analysis
Robust estimation
spellingShingle Fisher-consistency
Functional data
Method of sieves
Outliers
Penalization
Principal component analysis
Robust estimation
Bali, J.L.
Boente, G.
Tyler, D.E.
Wang, J.-L.
Robust functional principal components: A projection-pursuit approach
topic_facet Fisher-consistency
Functional data
Method of sieves
Outliers
Penalization
Principal component analysis
Robust estimation
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. © Institute of Mathematical Statistics, 2011.
format JOUR
author Bali, J.L.
Boente, G.
Tyler, D.E.
Wang, J.-L.
author_facet Bali, J.L.
Boente, G.
Tyler, D.E.
Wang, J.-L.
author_sort Bali, J.L.
title Robust functional principal components: A projection-pursuit approach
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
url http://hdl.handle.net/20.500.12110/paper_00905364_v39_n6_p2852_Bali
work_keys_str_mv AT balijl robustfunctionalprincipalcomponentsaprojectionpursuitapproach
AT boenteg robustfunctionalprincipalcomponentsaprojectionpursuitapproach
AT tylerde robustfunctionalprincipalcomponentsaprojectionpursuitapproach
AT wangjl robustfunctionalprincipalcomponentsaprojectionpursuitapproach
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