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Sensitivity Coefficient in Principal Component Analysis: Robust Case

In the classical principal component analysis (PCA), the empirical influence function for the sensitivity coefficient ρ is used to detect influential observations on the subspace spanned by the dominants principal components. In this article, we derive the influence function of ρ in the case where the reweighted minimum covariance determinant (MCD1) is used as estimator of multivariate location and scatter. Our aim is to confirm the reliability in terms of robustness of the MCD1 via the approach based on the influence function of the sensitivity coefficient.