  A weighted likelihood approach for robust fitting of a mixture of
multivariate Gaussian components is developed in this work. Two approaches have
been proposed that are driven by a suitable modification of the standard EM and
CEM algorithms, respectively. In both techniques, the M-step is enhanced by the
computation of weights aimed at downweighting outliers. The weights are based
on Pearson residuals stemming from robust Mahalanobis-type distances. Formal
rules for robust clustering and outlier detection can be also defined based on
the fitted mixture model. The behavior of the proposed methodologies has been
investigated by some numerical studies and real data examples in terms of both
fitting and classification accuracy and outlier detection.
