Zero-Inflated Poisson model has found a wide variety of applications in recent years in statistical analyses of count data, especially in count regression models. Zero-Inflated Poisson model is characterized in this paper through a linear differential equation satisfied by its probability generating function [1] [2].
Zero-inflated negative binomial distribution is characterized in this paper through a linear differential equation satisfied by its probability generating function.
Abstract:Zero-inflated probability models have been applied to a variety of situations in the recent years. Especially they are found very useful in count regression modeling. The zero-inflated binomial model is characterized in this paper through a differential equation which is satisfied by its probability generating function.
Distributions are useful to model random phenomena. New random experiments are conducted and new data sets are encountered. In turn, new distributions emerge. Zero-inflated discrete models are such examples and they are useful in various situations. Further, they have also been characterized [see Nanjundan and Sadiq Pasha (2018)]. Zero-inflated continuous distributions are discussed in the context of insurance portfolio. Zero-inflated gamma distribution is characterized in this paper through a differential equation satisfied by its moment generating function.
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