Summary
This paper deals with the stability of robust principal components analysis (PCA) defined through robust estimates of the population covariance matrix as M-estimators or the MVE-estimator. The stability is measured by means of an angular measure between sample principal components and population principal components, the latter being obtained by bootstraping. The studies performed on different data sets show that robust methods do not always improve the stability of PCA. This allows the statistician to choose between robust and nonrobust PCA.