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

Abstract

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.