Identifiability; Finite Mixture Models; Normal Distribution
Résumé :
[en] In this paper, we show under which conditions generalized finite mixture with underlying normal distribution are identifiable in the sense that a given dataset leads to a uniquely determined set of model parameter estimations up to a permuta-tion of the clusters.
Disciplines :
Mathématiques
Auteur, co-auteur :
NOEL, Cédric ; Université de Lorraine > IUT de Thionville-Yutz
SCHILTZ, Jang ; University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Finance (DF)
Langue du document :
Anglais
Titre :
Identifiability of Finite Mixture Models with underlying Normal Distribution
