Multilevel zero-inflated Generalized Poisson regression modeling for dispersed correlated count data

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• 作者：Afshin Almasi^a ; Mohammad Reza Eshraghian^b ; ^{eshraghianmr@yahoo.com" class="auth_mail" title="E-mail the corresponding author} ; Abbas Moghimbeigi^c ; Abbas Rahimi^b ; Kazem Mohammad^b ; Sadegh Fallahigilan^d
• 关键词：Count data ; EM algorithm ; Multilevel ; Generalized Poisson regression ; Zero-inflation ; Dispersion ; Monte Carlo simulation
• 刊名：Statistical Methodology
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：30
• 期：Complete
• 页码：1-14
• 全文大小：413 K

文摘

Poisson or zero-inflated Poisson models often fail to fit count data either because of over- or underdispersion relative to the Poisson distribution. Moreover, data may be correlated due to the hierarchical study design or the data collection methods. In this study, we propose a multilevel zero-inflated generalized Poisson regression model that can address both over- and underdispersed count data. Random effects are assumed to be independent and normally distributed. The method of parameter estimation is EM algorithm base on expectation and maximization which falls into the general framework of maximum-likelihood estimations. The performance of the approach was illustrated by data regarding an index of tooth caries on 9-year-old children. Using various dispersion parameters, through Monte Carlo simulations, the multilevel ZIGP yielded more accurate parameter estimates, especially for underdispersed data.