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Trimming algorithms for clustering contaminated grouped data and their robustness. (English) Zbl 1284.62372

Summary: We establish an affine equivariant, constrained heteroscedastic model and criterion with trimming for clustering contaminated, grouped data. We show existence of the maximum likelihood estimator, propose a method for determining an appropriate constraint, and design a strategy for finding reasonable clusterings. We finally compute breakdown points of the estimated parameters thereby showing asymptotic robustness of the method.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62F35 Robustness and adaptive procedures (parametric inference)
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