The multivariate component zero-inflated Poisson model for correlated count data analysis

Qin Wu, Guo-Liang Tian, Jinyu Wang, Tao Li, Man Lai Tang, Chi Zhang

Research output: Contribution to journalArticlepeer-review


Multivariate zero-inflated Poisson (ZIP) distributions are important tools for modelling and analysing correlated count data with extra zeros. Unfortunately, existing multivariate ZIP distributions consider only the overall zero-inflation while the component zero-inflation is not well addressed. This paper proposes a flexible multivariate ZIP distribution, called the multivariate component ZIP distribution, in which both the overall and component zero-inflations are taken into account. Likelihood-based inference procedures including the calculation of maximum likelihood estimates of parameters in the model without and with covariates are provided. Simulation studies indicate that the performance of the proposed methods on the multivariate component ZIP model is satisfactory. The Australia health care utilisation data set is analysed to demonstrate that the new distribution is more appropriate than the existing multivariate ZIP distributions.
Original languageEnglish
Pages (from-to)234 - 261
JournalAustralian and New Zealand Journal of Statistics
Issue number3
Early online date27 Aug 2023
Publication statusPublished - 30 Sept 2023


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