A new bivariate Poisson distribution via conditional specification: properties and applications

Indranil Ghosh, Filipe Marques, Subrata Chakraborty

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this article, we discuss a bivariate Poisson distribution whose conditionals are univariate Poisson distributions and the marginals are not Poisson which exhibits negative correlation. Some useful structural properties of this distribution namely marginals, moments, generating functions, stochastic ordering are investigated. Simple proofs of negative correlation, marginal over-dispersion, distribution of sum and conditional given the sum are also derived. The distribution is shown to be a member of the multi-parameter exponential family and some natural but useful consequences are also outlined. Parameter estimation with maximum likelihood is implemented. Copula-based simulation experiments are carried out using Bivariate Normal and the Farlie–Gumbel–Morgenstern copulas to assess how the model behaves in dealing with the situation. Finally, the distribution is fitted to seven bivariate count data sets with an inherent negative correlation to illustrate suitability.

Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalJournal of Applied Statistics
Publication statusPublished - Jul 2020


  • bivariate copula
  • Bivariate Poisson distribution
  • conditional specification
  • copula-based simulation
  • English premier league data
  • negative correlation
  • seeds and plant grown data


Dive into the research topics of 'A new bivariate Poisson distribution via conditional specification: properties and applications'. Together they form a unique fingerprint.

Cite this