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Determining the number of components in mixture regression models: an experimental design

Author

Listed:
  • Ana Brochado

    (Instituto Universitário de Lisboa (ISCTE-IUL), DINÂMIA CET – IUL)

  • Vitorino Martins

    (Faculdade de Economia da Universidade do Porto (FEP-UP))

Abstract

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.

Suggested Citation

  • Ana Brochado & Vitorino Martins, 2020. "Determining the number of components in mixture regression models: an experimental design," Economics Bulletin, AccessEcon, vol. 40(2), pages 1465-1474.
    • Handle: RePEc:ebl:ecbull:eb-20-00111
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    More about this item

    Keywords

    Information criterion; classification criterion; component; experimental design; simulation.;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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