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.

CEMAT - Center for Computational and Stochastic Mathematics