Handling of over-dispersion of count data via truncation using zero-inflated poisson regression model
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 s...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2011
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| Subjects: | |
| 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 |
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| Summary: | 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. |
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