Robust functional principal component analysis

When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator sn may be used in the maximization proble...

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Detalles Bibliográficos
Autores principales: Bali, J.L., Boente, G.
Formato: SER
Materias:
Covariance Operator
Functional Data Analysis
Principal Direction
Robust Estimator
Schmidt Operator
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_21947767_v_n_p41_Bali
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_21947767_v_n_p41_Bali
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spelling todo:paper_21947767_v_n_p41_Bali2023-10-03T16:40:22Z Robust functional principal component analysis Bali, J.L. Boente, G. Covariance Operator Functional Data Analysis Principal Direction Robust Estimator Schmidt Operator When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator sn may be used in the maximization problem. In this paper, we review some of the proposed approaches to robust functional PCA including one which adapts the projection pursuit approach to the functional data setting. © 2014, Springer International Publishing Switzerland. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_21947767_v_n_p41_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 Covariance Operator
Functional Data Analysis
Principal Direction
Robust Estimator
Schmidt Operator
spellingShingle Covariance Operator
Functional Data Analysis
Principal Direction
Robust Estimator
Schmidt Operator
Bali, J.L.
Boente, G.
Robust functional principal component analysis
topic_facet Covariance Operator
Functional Data Analysis
Principal Direction
Robust Estimator
Schmidt Operator
description When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator sn may be used in the maximization problem. In this paper, we review some of the proposed approaches to robust functional PCA including one which adapts the projection pursuit approach to the functional data setting. © 2014, Springer International Publishing Switzerland.
format SER
author Bali, J.L.
Boente, G.
author_facet Bali, J.L.
Boente, G.
author_sort Bali, J.L.
title Robust functional principal component analysis
title_short Robust functional principal component analysis
title_full Robust functional principal component analysis
title_fullStr Robust functional principal component analysis
title_full_unstemmed Robust functional principal component analysis
title_sort robust functional principal component analysis
url http://hdl.handle.net/20.500.12110/paper_21947767_v_n_p41_Bali
work_keys_str_mv AT balijl robustfunctionalprincipalcomponentanalysis
AT boenteg robustfunctionalprincipalcomponentanalysis
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