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A Transition Model for Analysis of Zero-Inflated Longitudinal Count Data Using Generalized Poisson Regression Model

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Resumo:In most of the longitudinal studies, involving count responses, excess zeros are common in practice. Usually, the current response measurement in a longitudinal sequence is a function of previous outcomes. For example, in a study about acute renal allograft rejection, the number of acute rejection episodes for a patient in current time is a function of this outcome at previous follow-up times. In this paper, we consider a transition model for accounting the dependence of current outcome on the previous outcomes in the presence of excess zeros. We propose the use of the generalized Poisson distribution as a flexible distribution for considering overdispersion (or underdispersion). The maximum likelihood estimates of the parameters are obtained using the EM algorithm. Some simulation studies are performed for illustration of the proposed methods. Also, analysis of a real data set of a kidney allograft rejection study illustrates the application of the proposed model.
Autores principais:Baghfalaki , Taban
Outros Autores:Ganjali , Mojtaba
Assunto:count data EM algorithm generalized Poisson distribution longitudinal data transition models zero-inflated models
Ano:2020
País:portugal
Tipo de documento:artigo
Tipo de acesso:unknown
Instituição associada:Instituto Nacional de Estatística
Idioma:inglês
Origem:REVSTAT-Statistical Journal

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