0 0.2 0.4 0.6 0.8 0.5 1 1.5 2 2.5 3 MM residuals for response 1 MLE residuals for response 1 0 0.2 0.4 0.6 0.8 1 2 3 4 5 MM residuals for response 2 MLE residuals for response 2 Figure 6: QQ-plots of sorted absolute residuals of MM-estimates vs sorted abso- lute residuals of MLE for each component of the response. Left plot corresponds to P$_{2}$O$_{5}$ (first component) and right to PbO (second component). Criterion MLE τ -estimate MM-estimate MM-univariate Component 1 2 1 2 1 2 1 2 MSE 0.081 0.051 0.351 0.806 0.340 0.682 0.354 0.762 τ -scale 0.044 0.022 0.005 0.007 0.008 0.006 0.005 0.006 Table 5: Mean square error (MSE) and τ -scale of the prediction errors of the MLE, multivariate MM-estimate, τ -estimate and univariate MM-estimate for each com- ponent of the response, computed by cross-validation. square root quantiles of the chi-squared distribution with q degrees of freedom. For ease of comparison, the center panel shows the MM’s QQ-plot truncated to the size of the MLE’s QQ-plot. The latter shows a very good fit of the norms to the chi-squared distribution, and therefore points out no suspect points, while the MM-estimate’s QQ-plot indicates some 30 possible outliers, i.e. about 16% of the data. The MLE’s norms are in general smaller than the MM-estimate’s norms, but this does not mean that the former gives a better fit, since here the residuals are normalized by the respective estimated residual dispersion matrices ̂ Σ$_{0}$ . Figure 24