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The Poisson distribution is a popular model for count data.
Additional file 1 Marginalized Mixture Models for Count Data from Multiple Source Populations.
All differences are statistically significant (p<0.05 using Fisher's exact test for count data).
This example demonstrates the suitability of the sCMP class to serve as an exploratory tool for count data modeling.
This approach was originally proposed for linear regression, but has been adapted for count data models [31].
One might model arrests as Poisson or binomial variables but they are clearly not normal variables nor is a shift model appropriate for count data.
There are two main classes of time series models that have been developed recently for count data: state-space models and thinning models.
In the last few years, research on the time series models for count data has become a popular topic in the literature.
We consider another bivariate time series model for count data to decide the relationship of the bivariate claim counts among the different periods.
Also, for count data, Fisher ([1950]) considered using the sample index of dispersion for testing the appropriateness of a Poisson distribution for an observed variable Y.
with μ ij being the mean of the j th component distribution, is a popular finite mixture model for count data.
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