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Robust classification for skewed data. (English) Zbl 1284.62378

Summary: In this paper we propose a robust classification rule for skewed unimodal distributions. For low dimensional data, the classifier is based on minimizing the adjusted outlyingness to each group. In the case of high dimensional data, the robustified SIMCA method is adjusted for skewness. The robustness of the methods is investigated through different simulations and by applying it to some datasets.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)

Software:

LIBRA; AS 307; ROBPCA
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References:

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