Inference under functional proportional and common principal component models

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Boente, Graciela Lina, Rodríguez, Daniela Andrea, Sued, Mariela
Publicado:	2010
Materias:	Common principal components Eigenfunctions Functional data analysis Hilbert-Schmidt operators Kernel methods Proportional model
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0047259X_v101_n2_p464_Boente http://hdl.handle.net/20.500.12110/paper_0047259X_v101_n2_p464_Boente
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_0047259X_v101_n2_p464_Boente
record_format	dspace
spelling	paper:paper_0047259X_v101_n2_p464_Boente2023-06-08T15:05:34Z Inference under functional proportional and common principal component models Boente, Graciela Lina Rodríguez, Daniela Andrea Sued, Mariela 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. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0047259X_v101_n2_p464_Boente 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, Graciela Lina Rodríguez, Daniela Andrea Sued, Mariela 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.
author	Boente, Graciela Lina Rodríguez, Daniela Andrea Sued, Mariela
author_facet	Boente, Graciela Lina Rodríguez, Daniela Andrea Sued, Mariela
author_sort	Boente, Graciela Lina
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
publishDate	2010
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0047259X_v101_n2_p464_Boente http://hdl.handle.net/20.500.12110/paper_0047259X_v101_n2_p464_Boente
work_keys_str_mv	AT boentegracielalina inferenceunderfunctionalproportionalandcommonprincipalcomponentmodels AT rodriguezdanielaandrea inferenceunderfunctionalproportionalandcommonprincipalcomponentmodels AT suedmariela inferenceunderfunctionalproportionalandcommonprincipalcomponentmodels
_version_	1768543601090887680

Inference under functional proportional and common principal component models

Ejemplares similares