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A multivariate Poisson regression model for count data

Author

Listed:
  • J. M. Muñoz-Pichardo
  • R. Pino-Mejías
  • J. García-Heras
  • F. Ruiz-Muñoz
  • M. Luz González-Regalado

Abstract

We propose a new technique for the study of multivariate count data. The proposed model is applied to the study of the number of individuals several fossil species found in a set of geographical observation points. First, we are proposing a multivariate model based on the Poisson distributions, which allows positive and negative correlations between the components. We are extending the log-linear Poisson model in the multivariate case through the conditional distributions. For this model, we obtain the maximum likelihood estimates and compute several goodness of fit statistics. Finally we illustrate the application of the proposed method over data sets: various simulated data sets and a count data set of various fossil species.

Suggested Citation

  • J. M. Muñoz-Pichardo & R. Pino-Mejías & J. García-Heras & F. Ruiz-Muñoz & M. Luz González-Regalado, 2021. "A multivariate Poisson regression model for count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(13-15), pages 2525-2541, November.
    • Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2525-2541
    DOI: 10.1080/02664763.2021.1877637
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    Cited by:

    1. Juan M. Muñoz-Pichardo & Emilio D. Lozano-Aguilera & Antonio Pascual-Acosta & Ana M. Muñoz-Reyes, 2021. "Multiple Ordinal Correlation Based on Kendall’s Tau Measure: A Proposal," Mathematics, MDPI, vol. 9(14), pages 1-16, July.

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