Eprint first made available on ORBilu (E-prints, Working papers and Research blog)
Identifiability of Finite Mixture Models with underlying Normal Distribution
NOEL, Cédric; SCHILTZ, Jang
2020
 

Files


Full Text
2020_2 Identifiability.pdf
Author postprint (343.55 kB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



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

Statistics


Number of views
60 (1 by Unilu)
Number of downloads
128 (2 by Unilu)

Bibliography


Similar publications



Contact ORBilu