Data-driven penalty calibration: A case study for Gaussian mixture model selection
Cathy Maugis, Bertrand Michel (2012)
ESAIM: Probability and Statistics
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In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. (2011) 41–68] , a penalized likelihood criterion is proposed to select a Gaussian mixture model among a specific model collection. This criterion depends on unknown constants which have to be calibrated in practical situations. A “slope heuristics” method is described and experimented to deal with this practical problem. In a model-based clustering...