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Ana Brochado and Vitorino Martins
''Determining the number of components in mixture regression models: an experimental design''
( 2020, Vol. 40 No.2 )
Despite the popularity of mixture regression models, the decision of how many components to retain remains an open issue. This study thus sought to compare the performance of 26 information and classification criteria. Each criterion was evaluated in terms of that component's success rate. The research's full experimental design included manipulating 9 factors and 22 levels. The best results were obtained for 5 criteria: Akaike information criteria 3 (AIC3), AIC4, Hannan-Quinn information criteria, integrated completed likelihood (ICL) Bayesian information criteria (BIC) and ICL with BIC approximation. Each criterion's performance varied according to the experimental conditions.
Keywords: Information criterion, classification criterion, component, experimental design, simulation.
JEL: C4 - Econometric and Statistical Methods: Special Topics
C9 - Design of Experiments: General
Manuscript Received : Feb 11 2020 Manuscript Accepted : Jun 02 2020

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