Reference : Identifiability of Finite Mixture Models with underlying Normal Distribution
E-prints/Working papers : First made available on ORBilu
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/10993/45317
Identifiability of Finite Mixture Models with underlying Normal Distribution
English
Noel, Cédric mailto [Université de Lorraine > IUT de Thionville-Yutz]
Schiltz, Jang mailto [University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Finance (DF) >]
2020
13
No
[en] Identifiability ; Finite Mixture Models ; Normal Distribution
[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.
http://hdl.handle.net/10993/45317

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