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Assessing the Number of Components in Mixture Models: a Review

Author

Listed:
  • Ana Oliveira-Brochado

    (Faculdade de Economia, Universidade do Porto)

  • Francisco Vitorino Martins

    (Faculdade de Economia, Universidade do Porto)

Abstract

Despite the widespread application of finite mixture models, the decision of how many classes are required to adequately represent the data is, according to many authors, an important, but unsolved issue. This work aims to review, describe and organize the available approaches designed to help the selection of the adequate number of mixture components (including Monte Carlo test procedures, information criteria and classification-based criteria); we also provide some published simulation results about their relative performance, with the purpose of identifying the scenarios where each criterion is more effective (adequate).

Suggested Citation

  • Ana Oliveira-Brochado & Francisco Vitorino Martins, 2005. "Assessing the Number of Components in Mixture Models: a Review," FEP Working Papers 194, Universidade do Porto, Faculdade de Economia do Porto.
    • Handle: RePEc:por:fepwps:194
    as

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    File URL: http://www.fep.up.pt/investigacao/workingpapers/05.11.03_WP194_brochadovitorino.pdf
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    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. ANVUR e CUN: l’amore ai tempi della mediana?
      by Giuseppe De Nicolao in ROARS - Return on Academic Research on 2012-06-28 13:48:46

    Citations

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    Cited by:

    1. Ana Oliveira-Brochado & F. Vitorino Martins, 2006. "Examining the segment retention problem for the “Group Satellite” case," FEP Working Papers 220, Universidade do Porto, Faculdade de Economia do Porto.
    2. Said Benlakhdar & Mohammed Rziza & Rachid Oulad Haj Thami, 2022. "Statistical modeling of directional data using a robust hierarchical von mises distribution model: perspectives for wind energy," Computational Statistics, Springer, vol. 37(4), pages 1599-1619, September.
    3. J. Samuel Baixauli & Susana Alvarez, 2010. "The Role of Market-Implied Severity Modeling for Credit VaR," Annals of Economics and Finance, Society for AEF, vol. 11(2), pages 337-353, November.

    More about this item

    Keywords

    Finite mixture; number of mixture components; information criteria; simulation studies.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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