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Modeling Count Data Under the Influence Overdispersion by Poisson Birnbaum-Saunders Regression Model
Reza Pourmousa , Narjes Gilani
Abstract:   (1486 Views)

In this paper the mixed Poisson regression model is discussed and a Poisson Birnbaum-Saunders regression model is introduced consider the over-dispersion. The Birnbaum-Saunders distribution is the mixture of two the generalized inverse Gaussian distributions, therefore it can be considered as an extension of traditional models. Our proposed model has less dimensional parameter space than the Poisson- generalized inverse Gaussian regression model. We also show that the proposed model has a closed form for likelihood function and we obtain its moments. The EM algorithm is used to estimate the parameters and its efficiency is compared with conventional models by a simulation study. An analysis of a real data is provided for more illustration.

Keywords: Birnbaum-Saunders distribution, EM algorithm, Count data, Poisson regression models, Overdispersion, Mixed Poisson regression model
Full-Text [PDF 6379 kb]   (366 Downloads)    
Type of Study: Research | Subject: Theoritical Statistics
Received: 2015/12/27 | Accepted: 2016/12/31 | Published: 2017/02/20
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Pourmousa R, Gilani N. Modeling Count Data Under the Influence Overdispersion by Poisson Birnbaum-Saunders Regression Model . J. of Stat. Sci.. 2017; 11 (1)
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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