Robust estimation of mixtures of Skew Normal Distributions

Recently, observed departures from the classical Gaussian mixture model in real datasets motivated the introduction of mixtures of skew t, and remarkably widened the application of model based clustering and classification to great many real datasets. Unfortunately,when data contamination occurs, classical inference for these models could be severely affected. In this paper we introduce robust estimation of mixtures of skew normal, to resist sparse outliers and even pointwise contamination that may arise in data collection. Hence, in each component, the skewed nature of the data is explicitly modeled, while any departure from it is dealt by the robust approach. Some applications on real data show the effectiveness of the proposal