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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Detalles Bibliográficos
Publicado: 2002
Materias:
Asymptotic variance
Common principal components
Partial influence function
Projectionpursuit
Robust estimation
Robust scatter matrix
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00063444_v89_n4_p861_Boente
http://hdl.handle.net/20.500.12110/paper_00063444_v89_n4_p861_Boente
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_00063444_v89_n4_p861_Boente
record_format dspace
spelling paper:paper_00063444_v89_n4_p861_Boente2023-06-08T14:31:11Z Influence functions and outlier detection under the common principal components model: A robust approach 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. 2002 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00063444_v89_n4_p861_Boente 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
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.
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
publishDate 2002
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00063444_v89_n4_p861_Boente
http://hdl.handle.net/20.500.12110/paper_00063444_v89_n4_p861_Boente
_version_ 1768541544815525888

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