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
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
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
Descripción
Sumario: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.

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