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Biometrika 2002 89(4):861-875; doi:10.1093/biomet/89.4.861
© 2002 by Biometrika Trust
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Influence functions and outlier detection under the common principal components model: A robust approach

Graciela Boente1, Ana M.Pires2 and Isabel M.Rodrigues2

1 Conicet, Departamento de Matemática and Instituto de Cálculo, Ciudad Universitaria, Pabellón 1, Buenos Aires, C1428EHA, Argentina gboente{at}mate.dm.uba.ar 2 Departamento de Matemática, Instituto Superior Técnico, Av.Rovisco Pais, 1049-001 Lisboa, Portugal ana.pires{at}math.ist.utl.pt isabel.rodrigues{at}math.ist.utl.pt

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.

Key Words: Asymptotic variance; Common principal components; Partial influence function; Projection-pursuit; Robust estimation; Robust scatter matrix

Received August 2001. Revised February 2002


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