A Family of Finite Mixture Distributions for Modelling Dispersion in Count Data




Ong, Seng Huat
Sim, Shin Zhu
Liu, Shuangzhe
Srivastava, Hari M.

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This paper considers the construction of a family of discrete distributions with the flexibility to cater for under-, equi- and over-dispersion in count data using a finite mixture model based on standard distributions. We are motivated to introduce this family because its simple finite mixture structure adds flexibility and facilitates application and use in analysis. The family of distributions is exemplified using a mixture of negative binomial and shifted negative binomial distributions. Some basic and probabilistic properties are derived. We perform hypothesis testing for equi-dispersion and simulation studies of their power and consider parameter estimation via maximum likelihood and probability-generating-function-based methods. The utility of the distributions is illustrated via their application to real biological data sets exhibiting under-, equi- and over dispersion. It is shown that the distribution fits better than the well-known generalized Poisson and COM–Poisson distributions for handling under-, equi- and over-dispersion in count data.



convolution, dispersion, Conway–Maxwell–Poisson, generalized Poisson, inverse trino- mial, negative binomial, goodness-of-fit, og-concavity, parameter estimation, score and likelihood ratio tests


Ong, S. H., Sim, S. Z., Liu, S., & Srivastava, H. M. (2023). A Family of Finite Mixture Distributions for Modelling Dispersion in Count Data. Stats, 6(3), 942–955. https://doi.org/10.3390/stats6030059