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Within the framework of probability models for overdispersed count data, we propose the generalized fractional Poisson distribution gfPd , which is a natural generalization of the fractional Poisson distribution fPd , and the standard Poisson distribution. We derive some properties of gfPd and more specifically we study moments, limiting behavior and other features of fPd. The skewness suggests that fPd can be left-skewed, right-skewed or symmetric; this makes the model flexible and appealing in practice. We apply the model to real big count data and estimate the model parameters using maximum likelihood. The proposed class of distributions includes the well-known COM-Poisson and the hyper-Poisson models. We characterize conditions on the parameters allowing for overdispersion and underdispersion, and analyze two special cases of interest which have not yet appeared in the literature.

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Using Scct s Interest Model

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Using Scct s Interest Model

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Using Scct s Interest Model

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