Robust sieve estimators for functional canonical correlation analysis

In this paper, we propose robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by combining sieves and robust association measures, leading to Fisher-consistent estimators for appropriate choices of the association measure. Under regular...

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Detalles Bibliográficos
Autores principales: Alvarez, A., Boente, G., Kudraszow, N.
Formato: JOUR
Materias:
Canonical correlation
Fisher-consistency
Functional data
Robust estimation
Sieves
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_0047259X_v170_n_p46_Alvarez
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_0047259X_v170_n_p46_Alvarez
record_format dspace
spelling todo:paper_0047259X_v170_n_p46_Alvarez2023-10-03T14:52:23Z Robust sieve estimators for functional canonical correlation analysis Alvarez, A. Boente, G. Kudraszow, N. Canonical correlation Fisher-consistency Functional data Robust estimation Sieves In this paper, we propose robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by combining sieves and robust association measures, leading to Fisher-consistent estimators for appropriate choices of the association measure. Under regularity conditions, the resulting estimators are consistent. The robust procedure allows us to construct detection rules to identify possible influential observations. The finite sample performance is illustrated through a simulation study in which contaminated data is included. The benefits of considering robust estimators are also illustrated on a real data set where the detection methods reveal the presence of influential observations for the first canonical directions that would be missed otherwise. © 2018 Elsevier Inc. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_0047259X_v170_n_p46_Alvarez
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Canonical correlation
Fisher-consistency
Functional data
Robust estimation
Sieves
spellingShingle Canonical correlation
Fisher-consistency
Functional data
Robust estimation
Sieves
Alvarez, A.
Boente, G.
Kudraszow, N.
Robust sieve estimators for functional canonical correlation analysis
topic_facet Canonical correlation
Fisher-consistency
Functional data
Robust estimation
Sieves
description In this paper, we propose robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by combining sieves and robust association measures, leading to Fisher-consistent estimators for appropriate choices of the association measure. Under regularity conditions, the resulting estimators are consistent. The robust procedure allows us to construct detection rules to identify possible influential observations. The finite sample performance is illustrated through a simulation study in which contaminated data is included. The benefits of considering robust estimators are also illustrated on a real data set where the detection methods reveal the presence of influential observations for the first canonical directions that would be missed otherwise. © 2018 Elsevier Inc.
format JOUR
author Alvarez, A.
Boente, G.
Kudraszow, N.
author_facet Alvarez, A.
Boente, G.
Kudraszow, N.
author_sort Alvarez, A.
title Robust sieve estimators for functional canonical correlation analysis
title_short Robust sieve estimators for functional canonical correlation analysis
title_full Robust sieve estimators for functional canonical correlation analysis
title_fullStr Robust sieve estimators for functional canonical correlation analysis
title_full_unstemmed Robust sieve estimators for functional canonical correlation analysis
title_sort robust sieve estimators for functional canonical correlation analysis
url http://hdl.handle.net/20.500.12110/paper_0047259X_v170_n_p46_Alvarez
work_keys_str_mv AT alvareza robustsieveestimatorsforfunctionalcanonicalcorrelationanalysis
AT boenteg robustsieveestimatorsforfunctionalcanonicalcorrelationanalysis
AT kudraszown robustsieveestimatorsforfunctionalcanonicalcorrelationanalysis
_version_ 1807319231000215552

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