Asymptotic theory for robust principal components

The asymptotic distribution of the eigenvalues and eigenvectors of the robust scatter matrix proposed by R. Maronna in 1976 is given when the observations are from an ellipsoidal distribution. The elements of each characteristic vector are the coefficients of a robustified version of principal components. We give a definition for the asymptotic efficiency of these estimators and we evaluate their influence curve. The problem of maximizing the efficiency under a bound on the influence curve is solved. Numerically, we calibrate the optimal estimators under the multivariate normal distribution and we evaluate their sensitivity.