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Identifiability of Finite Mixture Models with underlying Normal Distribution
Noel, Cédric; Schiltz, Jang
2020
 

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Keywords :
Identifiability; Finite Mixture Models; Normal Distribution
Abstract :
[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 :
Mathematics
Author, co-author :
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)
Language :
English
Title :
Identifiability of Finite Mixture Models with underlying Normal Distribution
Publication date :
2020
Number of pages :
13
Available on ORBilu :
since 04 January 2021

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