general more concentrated near the origin. In general, we may conclude that the MM-estimate gives a good fit to the bulk of the data, at the expense of misfitting a reduced proportion of atypical points, while the MLE tries to fit all data points, including the atypical ones, with the cost of a poor fit to the bulk of the data. We compared the predictive behaviors of the MLE and the MM-estimates through five-fold cross validation. We also included the univariate MM-estimates corresponding to each component of the response and the τ -estimate proposed by Garca Ben et al. [8]. As initial estimate for the univariate MM-estimates we use S-estimates. The τ -, S- and the univariate MM-estimates employ bisquare ρ -functions with constants such that the univariate MM- and τ -estimates have Gaussian ARE equal to 0.95 and the S-estimate has breakdown point 0.5. We considered two evaluation criteria: the mean squared error (MSE) and a robust criterion, namely a τ -scale (8.2) of the predictive errors, both computed separately for each component of the response. In the τ -scale s is an M-scale with breakdown point 0.5 and ρ$_{2}$ is a bisquare ρ -function with constant such that the τ -scale has Gaussian asymptotic efficiency equal to 0.85. Table 5 shows the results. According to the MSE, the MLE is much better than the robust estimates. However, the τ -scales yield the opposite conclusion. The reason of this fact is the MSE’s sensitivity to outliers. This result shows how misleading a non-robust criterion may be. According to the τ -scale, the predictive performance of our MM-estimate for the second component is slightly better than that of the τ -estimate, while the opposite occurs for the first component. The results obtained with the univariate MM-estimates are similar to those of the multivariate MM. The QQ-plots in Figure 8 compare for each response component the absolute values of the sorted cross validation prediction errors of our MM-estimate with 26