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

Author:

Wu Qin1ORCID,Tian Guo‐Liang2,Li Tao2,Tang Man‐Lai3,Zhang Chi4ORCID

Affiliation:

1. Department of Statistics, School of Mathematical Sciences South China Normal University Guangzhou Guangdong 510631 People's Republic of China

2. Department of Statistics and Data Science Southern University of Science and Technology Shenzhen Guangdong 518055 People's Republic of China

3. Department of Physics, Astronomy and Mathematics, School of Physics, Engineering & Computer Science University of Hertfordshire Hertfordshire UK

4. College of Economics Shenzhen University Shenzhen Guangdong 518055 People's Republic of China

Abstract

SummaryMultivariate 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.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

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