Handling of OverDispersion of Count Data via Truncation using Poisson Regression Model
Abstract
A Poisson model typically is assumed for count data. It is assumed to have the same value for expectation and variance in a Poisson distribution, but most of the time there is overdispersion in the model. Furthermore, the response variable in such cases is truncated for some outliers or large values. In this paper, a Poisson regression model is introduced on truncated data. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodnessoffit for the regression model is examined. We study the effects of truncation in terms of parameters estimation and their standard errors via real data.
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Saffari, S.; Adnan, R.; Greene, William; Ahmad, M. (2013)Typically, a Poisson regression model is assumed for count data. In many cases, there are many zeros in the dependent variable, therefore the mean is not equal to the variance value of the dependent variable. Thus, we ...

Greene, William; Saffari, S.E.; Adnan, R. (2012)A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the ...

Saffari, S.; Adnan, R.; Greene, William (2012)A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the ...