Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 data

SCHILTZ, Jang; NOEL, Cédric

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Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 data

SCHILTZ, Jang; NOEL, Cédric

2025 • In Stemmler, Mark; Wiedermann, Wolfgang; Huang, Francis L. (Eds.) Dependent Data in Social Sciences Research

Peer reviewed

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https://hdl.handle.net/10993/50948

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Disciplines :

Computer science

Author, co-author :

SCHILTZ, Jang ; University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Finance (DF)

NOEL, Cédric ; University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) ; University of Lorraine

External co-authors :

yes

Language :

English

Title :

Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 data

Publication date :

October 2025

Main work title :

Dependent Data in Social Sciences Research

Author, co-author :

Stemmler, Mark; FAU - Friedrich-Alexander-Universität Erlangen-Nürnberg > Institute of Psychology

Wiedermann, Wolfgang; MU - University of Missouri-Columbia > Educational, School and Counseling Psychology

Huang, Francis L.; MU - University of Missouri-Columbia > Educational, School and Counseling Psychology

Publisher :

Springer Nature, Switzerland

Edition :

Second Edition

ISBN/EAN :

978-3-031-56317-1

Pages :

127-158

Peer reviewed :

Peer reviewed

Focus Area :

Computational Sciences

Available on ORBilu :

since 05 May 2022

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