Handling of over-dispersion of count data via truncation using zero-inflated poisson regression model

• Main Authors: Saffari, Seyed Ehsan, Adnan, Robiah, Greene, William
• Format: Conference or Workshop Item
• Published: 2011
• Subjects: Q Science (General)
• Online Access: http://eprints.utm.my/id/eprint/45910/; https://www.researchgate.net/publication/257246180_Handling_of_Over-dispersion_of_Count_Data_via_Truncation_using_Poisson_Regression_Model
• Institution: Universiti Teknologi Malaysia

A Poisson model typically is assumed for count data. It is assumed to have the same value for expe ctation and variance in a Poisson distribution, but most of the time there is over - dispersion 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 introd uced 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 goodness - of - fit for the regression model is examined. We study the effects of truncation in terms of parameters estimation and their standard errors via real data.