Robust tests for the common principal components model

When dealing with several populations, the common principal components (CPC) model assumes equal principal axes but different variances along them. In this paper, a robust log-likelihood ratio statistic allowing to test the null hypothesis of a CPC model versus no restrictions on the scatter matrice...

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Autores principales: Boente, G., Pires, A.M., Rodrigues, I.M.
Formato: JOUR
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Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03783758_v139_n4_p1332_Boente
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spelling todo:paper_03783758_v139_n4_p1332_Boente2023-10-03T15:32:16Z Robust tests for the common principal components model Boente, G. Pires, A.M. Rodrigues, I.M. Common principal components Log-likelihood ratio test Plug-in methods Proportional scatter matrices Robust estimation Wald-type test When dealing with several populations, the common principal components (CPC) model assumes equal principal axes but different variances along them. In this paper, a robust log-likelihood ratio statistic allowing to test the null hypothesis of a CPC model versus no restrictions on the scatter matrices is introduced. The proposal plugs into the classical log-likelihood ratio statistic robust scatter estimators. Using the same idea, a robust log-likelihood ratio and a robust Wald-type statistic for testing proportionality against a CPC model are considered. Their asymptotic distributions under the null hypothesis and their partial influence functions are derived. A small simulation study allows to compare the behavior of the classical and robust tests, under normal and contaminated data. © 2008 Elsevier B.V. All rights reserved. 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_03783758_v139_n4_p1332_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
Log-likelihood ratio test
Plug-in methods
Proportional scatter matrices
Robust estimation
Wald-type test
spellingShingle Common principal components
Log-likelihood ratio test
Plug-in methods
Proportional scatter matrices
Robust estimation
Wald-type test
Boente, G.
Pires, A.M.
Rodrigues, I.M.
Robust tests for the common principal components model
topic_facet Common principal components
Log-likelihood ratio test
Plug-in methods
Proportional scatter matrices
Robust estimation
Wald-type test
description When dealing with several populations, the common principal components (CPC) model assumes equal principal axes but different variances along them. In this paper, a robust log-likelihood ratio statistic allowing to test the null hypothesis of a CPC model versus no restrictions on the scatter matrices is introduced. The proposal plugs into the classical log-likelihood ratio statistic robust scatter estimators. Using the same idea, a robust log-likelihood ratio and a robust Wald-type statistic for testing proportionality against a CPC model are considered. Their asymptotic distributions under the null hypothesis and their partial influence functions are derived. A small simulation study allows to compare the behavior of the classical and robust tests, under normal and contaminated data. © 2008 Elsevier B.V. All rights reserved.
format JOUR
author Boente, G.
Pires, A.M.
Rodrigues, I.M.
author_facet Boente, G.
Pires, A.M.
Rodrigues, I.M.
author_sort Boente, G.
title Robust tests for the common principal components model
title_short Robust tests for the common principal components model
title_full Robust tests for the common principal components model
title_fullStr Robust tests for the common principal components model
title_full_unstemmed Robust tests for the common principal components model
title_sort robust tests for the common principal components model
url http://hdl.handle.net/20.500.12110/paper_03783758_v139_n4_p1332_Boente
work_keys_str_mv AT boenteg robusttestsforthecommonprincipalcomponentsmodel
AT piresam robusttestsforthecommonprincipalcomponentsmodel
AT rodriguesim robusttestsforthecommonprincipalcomponentsmodel
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