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Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study

  • Martí Casals [1] ; Klaus Langohr [2] ; Josep Lluís Carrasco [1] ; Lars Rönnegård [3]
    1. [1] Universitat de Barcelona

      Universitat de Barcelona

      Barcelona, España

    2. [2] Universitat Politècnica de Catalunya

      Universitat Politècnica de Catalunya

      Barcelona, España

    3. [3] Dalarna University

      Dalarna University

      Suecia

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  • Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 39, Nº. 2, 2015, págs. 281-308
  • Idioma: inglés
  • Enlaces
  • Resumen

    • Generalized linear mixed models are flexible tools for modeling non-normal data and are useful for accommodating overdispersion in Poisson regression models with random effects. Their main difficulty resides in the parameter estimation because there is no analytic solution for the maximization of the marginal likelihood. Many methods have been proposed for this purpose and many of them are implemented in software packages. The purpose of this study is to compare the performance of three different statistical principles —marginal likelihood, extended likelihood, Bayesian analysis— via simulation studies. Real data on contact wrestling are used for illustration.

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