Influence functions and outlier detection under the common principal components model: A robust approach

The common principal components model for several groups of multivariate observations assumes equal principal axes but different variances along these axes among the groups. Influence functions for plug-in and projection-pursuit estimates under a common principal component model are obtained. Asympt...

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Autores principales: Boente, G., Pires, A.M., Rodrigues, I.M.
Formato: JOUR
Materias:
Asymptotic variance
Common principal components
Partial influence function
Projectionpursuit
Robust estimation
Robust scatter matrix
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00063444_v89_n4_p861_Boente
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_00063444_v89_n4_p861_Boente
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spelling todo:paper_00063444_v89_n4_p861_Boente2023-10-03T14:05:01Z Influence functions and outlier detection under the common principal components model: A robust approach Boente, G. Pires, A.M. Rodrigues, I.M. Asymptotic variance Common principal components Partial influence function Projectionpursuit Robust estimation Robust scatter matrix The common principal components model for several groups of multivariate observations assumes equal principal axes but different variances along these axes among the groups. Influence functions for plug-in and projection-pursuit estimates under a common principal component model are obtained. Asymptotic variances are derived from them. Outlier detection is possible using partial influence functions. © 2002 Biometrika Trust. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00063444_v89_n4_p861_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 Asymptotic variance
Common principal components
Partial influence function
Projectionpursuit
Robust estimation
Robust scatter matrix
spellingShingle Asymptotic variance
Common principal components
Partial influence function
Projectionpursuit
Robust estimation
Robust scatter matrix
Boente, G.
Pires, A.M.
Rodrigues, I.M.
Influence functions and outlier detection under the common principal components model: A robust approach
topic_facet Asymptotic variance
Common principal components
Partial influence function
Projectionpursuit
Robust estimation
Robust scatter matrix
description The common principal components model for several groups of multivariate observations assumes equal principal axes but different variances along these axes among the groups. Influence functions for plug-in and projection-pursuit estimates under a common principal component model are obtained. Asymptotic variances are derived from them. Outlier detection is possible using partial influence functions. © 2002 Biometrika Trust.
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 Influence functions and outlier detection under the common principal components model: A robust approach
title_short Influence functions and outlier detection under the common principal components model: A robust approach
title_full Influence functions and outlier detection under the common principal components model: A robust approach
title_fullStr Influence functions and outlier detection under the common principal components model: A robust approach
title_full_unstemmed Influence functions and outlier detection under the common principal components model: A robust approach
title_sort influence functions and outlier detection under the common principal components model: a robust approach
url http://hdl.handle.net/20.500.12110/paper_00063444_v89_n4_p861_Boente
work_keys_str_mv AT boenteg influencefunctionsandoutlierdetectionunderthecommonprincipalcomponentsmodelarobustapproach
AT piresam influencefunctionsandoutlierdetectionunderthecommonprincipalcomponentsmodelarobustapproach
AT rodriguesim influencefunctionsandoutlierdetectionunderthecommonprincipalcomponentsmodelarobustapproach
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