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Nonparametric consistency for maximum likelihood estimation and clustering based on mixtures of elliptically-symmetric distributions
The consistency of the maximum likelihood estimator for mixtures of elliptically-symmetric distributions for estimating its population version is shown, where the underlying distribution $P$ is nonparametric and does not necessarily belong to the class of mixtures on which the estimator is based.
Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score
Based on bootstrap resampling of the quadratic scores, we propose a selection rule that allows choosing among many clustering solutions.
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