We propose a new class of robust and Fisher-consistent estimators for mixture models. These estimators can be used to construct robust model-based clustering procedures. We study in detail the case of multivariate normal mixtures and propose a procedure that uses S estimators of multivariate location and scatter. We develop an algorithm to compute the estimators and to build the clusters which is quite similar to the EM algorithm. An extensive Monte Carlo simulation study shows that our proposal compares favorably with other robust and non robust model-based clustering procedures. We apply ours and alternative procedures to a real data set and again find that the best results are obtained using our proposal.

翻译：我们建议为混合模型建立一个新型的稳健和渔业一致的估测器。这些估测器可用于构建稳健的模型类集程序。我们详细研究多变正常混合物的情况,并提出使用多变位置和分散的估测器的程序。我们开发了一个算法来计算估测器和构建与EM算法相当相似的集群。一个广泛的蒙特卡洛模拟研究表明,我们的提案优于其他稳健和非稳健的模型类集程序。我们对真实的数据集应用我们的和替代程序,并再次发现最佳结果是利用我们的提案取得的。