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Computing with bivariate COM-Poisson model under different copulas

Author

Listed:
  • Mamode Khan Naushad

    (University of Mauritius, Reduit, Mauritius)

  • Rumjaun Wasseem

    (University of Mauritius, Reduit, Mauritius)

  • Sunecher Yuvraj

    (University of Technology, Port Louis, Mauritius)

  • Jowaheer Vandna

    (University of Mauritius, Reduit, Mauritius)

Abstract

Bivariate counts are collected in many sectors of research but the analysis of such data is often challenging because each series of counts may exhibit different levels and types of dispersion. This paper addresses this problem by proposing a flexible bivariate COM-Poisson model that may handle any combination of over-, equi- and under-dispersion at any levels. In this paper, the bivariate COM-Poisson is developed via Archimedean copulas. The Generalized Quasi-Likelihood (GQL) approach is used to estimate the unknown mean parameters in the copula-based bivariate COM-Poisson model while the dependence parameter is estimated using the copula likelihood. We further introduce a Monte Carlo experiment to generate bivariate COM-Poisson data under different dispersion levels. The performance of the GQL approach is assessed on the simulated data. The model is applied to analyze real-life epileptic seizures data.

Suggested Citation

  • Mamode Khan Naushad & Rumjaun Wasseem & Sunecher Yuvraj & Jowaheer Vandna, 2017. "Computing with bivariate COM-Poisson model under different copulas," Monte Carlo Methods and Applications, De Gruyter, vol. 23(2), pages 131-146, June.
    • Handle: RePEc:bpj:mcmeap:v:23:y:2017:i:2:p:131-146:n:1
    DOI: 10.1515/mcma-2017-0103
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    References listed on IDEAS

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