Inference under functional proportional and common principal component models

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In many situations, when dealing with several populations with different covariance operators, equality of the operators is assumed. Usually, if this assumption does not hold, one estimates the covariance operator of each group separately, which leads to a large number of parameters. As in the multi...

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Autores principales: Boente, G., Rodriguez, D., Sued, M.
Formato: JOUR
Materias:
Common principal components
Eigenfunctions
Functional data analysis
Hilbert-Schmidt operators
Kernel methods
Proportional model
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_0047259X_v101_n2_p464_Boente
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_0047259X_v101_n2_p464_Boente
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spelling todo:paper_0047259X_v101_n2_p464_Boente2023-10-03T14:52:21Z Inference under functional proportional and common principal component models Boente, G. Rodriguez, D. Sued, M. Common principal components Eigenfunctions Functional data analysis Hilbert-Schmidt operators Kernel methods Proportional model In many situations, when dealing with several populations with different covariance operators, equality of the operators is assumed. Usually, if this assumption does not hold, one estimates the covariance operator of each group separately, which leads to a large number of parameters. As in the multivariate setting, this is not satisfactory since the covariance operators may exhibit some common structure. In this paper, we discuss the extension to the functional setting of the common principal component model that has been widely studied when dealing with multivariate observations. Moreover, we also consider a proportional model in which the covariance operators are assumed to be equal up to a multiplicative constant. For both models, we present estimators of the unknown parameters and we obtain their asymptotic distribution. A test for equality against proportionality is also considered. © 2009 Elsevier Inc. All rights reserved. Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Rodriguez, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Sued, M. 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_0047259X_v101_n2_p464_Boente
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Common principal components
Eigenfunctions
Functional data analysis
Hilbert-Schmidt operators
Kernel methods
Proportional model
spellingShingle Common principal components
Eigenfunctions
Functional data analysis
Hilbert-Schmidt operators
Kernel methods
Proportional model
Boente, G.
Rodriguez, D.
Sued, M.
Inference under functional proportional and common principal component models
topic_facet Common principal components
Eigenfunctions
Functional data analysis
Hilbert-Schmidt operators
Kernel methods
Proportional model
description In many situations, when dealing with several populations with different covariance operators, equality of the operators is assumed. Usually, if this assumption does not hold, one estimates the covariance operator of each group separately, which leads to a large number of parameters. As in the multivariate setting, this is not satisfactory since the covariance operators may exhibit some common structure. In this paper, we discuss the extension to the functional setting of the common principal component model that has been widely studied when dealing with multivariate observations. Moreover, we also consider a proportional model in which the covariance operators are assumed to be equal up to a multiplicative constant. For both models, we present estimators of the unknown parameters and we obtain their asymptotic distribution. A test for equality against proportionality is also considered. © 2009 Elsevier Inc. All rights reserved.
format JOUR
author Boente, G.
Rodriguez, D.
Sued, M.
author_facet Boente, G.
Rodriguez, D.
Sued, M.
author_sort Boente, G.
title Inference under functional proportional and common principal component models
title_short Inference under functional proportional and common principal component models
title_full Inference under functional proportional and common principal component models
title_fullStr Inference under functional proportional and common principal component models
title_full_unstemmed Inference under functional proportional and common principal component models
title_sort inference under functional proportional and common principal component models
url http://hdl.handle.net/20.500.12110/paper_0047259X_v101_n2_p464_Boente
work_keys_str_mv AT boenteg inferenceunderfunctionalproportionalandcommonprincipalcomponentmodels
AT rodriguezd inferenceunderfunctionalproportionalandcommonprincipalcomponentmodels
AT suedm inferenceunderfunctionalproportionalandcommonprincipalcomponentmodels
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