0.0955
   d=200   0.0148     -      0.0200   0.0209    0.0246   0.0275   0.0895
  ------- -------- -------- -------- --------- -------- -------- --------

  : Median estimation errors, according to criterion $R(\widehat{\mathbf{P}}_q, \mathbf{P}_q) $ with a dimension $q=2$, for non contaminated samples of size $n=200$, over 500 Monte Carlo experiments.[]{data-label="tab:summary_mu0"}

The median errors of estimation of the eigenspace generated by the first two eigenvectors ($q=2$), according to criterion (\[def:errvecp\]), are given in Table \[tab:summary\_mu0\] for non contaminated data ($\delta=0$). The distribution of the estimation error $R(\widehat{\mathbf{P}}_q, \mathbf{P}_q)$ is drawn for the different approaches in Figure \[fig:boxerr0\] when the dimension is not large ($d=50$). As expected, the “Oracle”, which is the classical PCA in this situation, provides the best estimations of the eigenspaces. Then, the MCD and the median covariation matrix, estimated by the Weiszfeld algorithm or the modified MCM(R+) recursive estimator, behave well and similarly. Note that when the dimension gets larger, the MCD cannot be used anymore and the MCM is the more effective robust estimator of the eigenspaces.

When the data are contaminated, the median errors of estimation of the eigenspace generated by the first two eigenvectors ($q=2$), according to criterion (\[def:errvecp\]), are given in Table \[tab:summary\_mu\]. In Figure \[fig:boxerr\], the distribution of the estimation 