Clustering and Model Selection via Penalized Likelihood for Different-sized Categorical Data Vectors

In this study, we consider unsupervised clustering of categorical vectors that can be of different size using mixture. We use likelihood maximization to estimate the parameters of the underlying mixture model and a penalization technique to select the number of mixture components. Regardless of the...

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Bibliographische Detailangaben

Hauptverfasser:	Derman, Esther, Pennec, Erwan Le
Format:	Artikel
Sprache:	eng
Schlagworte:	Mathematics - Statistics Theory Statistics - Theory
Online-Zugang:	Volltext bestellen
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