Abstract: The Maximum Likelihood Estimator (MLE) has been widely used to estimate the unknown parameters in the finite mixture of Generalized Linear Models (GLMs). However, the MLE can be very sensitive to outliers in the data. In this paper we consider an approach based on the Trimmed Likelihood Estimator (TLE) to estimate mixtures of GLMs in a robust way. The superiority of this approach in comparison with the MLE is illustrated through a simulation study.
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