0 1 2 3 10 20 30 40 50 60 Norms of MM residuals Sq. root of  2 $_{q}$ quantiles 0 1 2 3 1 2 3 4 5 Norms of MM residuals Sq. root of  2 $_{q}$ quantiles 0 1 2 3 1 2 3 4 5 Norms of MLE residuals Sq. root of  2 $_{q}$ quantiles Figure 5: QQ-plots of the Mahalanobis norms of the residuals of the MM-estimate (left), the MLE (right) and the MM-estimate in the same interval as the MLE (center). Table 4: MLE and MM-estimate of the covariance matrix of the errors. MLE MM-estimate ( 0 . 0645 − 0 . 0008 − 0 . 0008 0 . 0348 ) ( 0 . 0102 − 0 . 0014 − 0 . 0014 0 . 0084 ) estimate. As initial estimate for the MM-estimate we use an S-estimate. The S- and the MM-estimates employ bisquare ρ -functions with constants such that the MM-estimate has Gaussian ARE equal to 0.95 and the S-estimate has breakdown point 0.5. In Figure 5 we present QQ-plots of the Mahalanobis norms of the residuals of the MLE and the MM-estimate against the root quantiles of the chi- squared distribution with q degrees of freedom. The QQ-plot of the MM-estimate shows clear outliers. In Figure 6 are compared the sorted absolute values of the residuals of the MLE with those corresponding to the MM-estimator for each component of the response. The right and left panels of Figure 5 show respectively the QQ-plots of the Mahalanobis norms of the residuals of the MLE and the MM-estimate against the 23