A multivariate zero-inflated binomial model for the analysis of correlated proportional data

Dianliang Deng, Yiguang Sun and Guo-Liang Tian

Journal of Applied Statistics, 2022, vol. 49, issue 11, 2740-2766

Abstract: In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.

Date: 2022
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DOI: 10.1080/02664763.2021.1918649

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