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Multivariate models for correlated count data

Author

Listed:
  • Mariana Rodrigues-Motta
  • Hildete P. Pinheiro
  • Eduardo G. Martins
  • M�rcio S. Araújo
  • S�rgio F. dos Reis

Abstract

In this study, we deal with the problem of overdispersion beyond extra zeros for a collection of counts that can be correlated. Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial distributions have been considered. First, we propose a multivariate count model in which all counts follow the same distribution and are correlated. Then we extend this model in a sense that correlated counts may follow different distributions. To accommodate correlation among counts, we have considered correlated random effects for each individual in the mean structure, thus inducing dependency among common observations to an individual. The method is applied to real data to investigate variation in food resources use in a species of marsupial in a locality of the Brazilian Cerrado biome.

Suggested Citation

  • Mariana Rodrigues-Motta & Hildete P. Pinheiro & Eduardo G. Martins & M�rcio S. Araújo & S�rgio F. dos Reis, 2013. "Multivariate models for correlated count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1586-1596, July.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1586-1596
    DOI: 10.1080/02664763.2013.789098
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    Cited by:

    1. Wagner Hugo Bonat & Bent Jørgensen, 2016. "Multivariate covariance generalized linear models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 649-675, November.
    2. W. H. Bonat & J. Olivero & M. Grande-Vega & M. A. Farfán & J. E. Fa, 2017. "Modelling the Covariance Structure in Marginal Multivariate Count Models: Hunting in Bioko Island," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 446-464, December.
    3. Jenni Niku & David I. Warton & Francis K. C. Hui & Sara Taskinen, 2017. "Generalized Linear Latent Variable Models for Multivariate Count and Biomass Data in Ecology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 498-522, December.

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