Finite mixtures of generalized linear models are commonly fitted by maximum likelihood and the EM algorithm. The estimation process and subsequent inferential and classification procedures can be badly affected by the occurrence of outliers. Actually, contamination in the sample at hand may lead to severely biased fitted components and poor classification accuracy. In order to take into account the potential presence of outliers, a robust fitting strategy is proposed that is based on the weighted likelihood methodology. The technique exhibits a satisfactory behavior in terms of both fitting and classification accuracy, as confirmed by some numerical studies and real data examples.

广义线性模型的有限混合通常由最大似然和 EM 算法拟合。估计过程以及随后的推理和分类程序可能会受到异常值的严重影响。实际上，手头样本中的污染可能会导致拟合分量严重偏差和分类精度差。为了考虑异常值的潜在存在，提出了一种基于加权似然方法的稳健拟合策略。正如一些数值研究和真实数据示例所证实的那样，该技术在拟合和分类精度方面表现出令人满意的行为。