Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering

The two main topics of this article are the introduction of the "optimally tuned robust improper maximum likelihood estimator" (OTRIMLE) for robust clustering based on the multivariate Gaussian model for clusters, and a comprehensive simulation study comparing the OTRIMLE to maximum likeli...

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Veröffentlicht in:	Journal of the American Statistical Association 2016-12, Vol.111 (516), p.1648-1659
Hauptverfasser:	Coretto, Pietro, Hennig, Christian
Format:	Artikel
Sprache:	eng
Schlagworte:	Cluster analysis EM-algorithm Estimating techniques Improper density Methods Mixture models Model-based clustering Noise Robustness Statistics Theory and Methods
Online-Zugang:	Volltext
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