Exponential dispersion models for overdispersed zero-inflated count data

Shaul K. Bar-Lev, Ad Ridder*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


We consider two classes of exponential dispersion models of discrete probability distributions which are defined by specifying their variance functions in their mean value parameterization. These classes were considered in our earlier paper as models of overdispersed zero-inflated distributions. In this paper we analyze the application of these classes to fit count data having overdispersed and zero-inflated statistics. For this reason, we first elaborate on the computational aspects of the probability distributions, before we consider the data fitting with our models. We execute an extensive comparison with other statistical models that are recently proposed, on both real data sets, and simulated data sets. Our findings are that our framework is a flexible tool that gives excellent results in a wide range of cases. Moreover, specifically when the data characteristics show also large skewness and kurtosis our models perform best.

Original languageEnglish
Pages (from-to)3286-3304
JournalCommunications in Statistics: Simulation and Computation
Issue number7
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.

Copyright 2021 Elsevier B.V., All rights reserved.


  • Count data analysis
  • Exponential dispersion models
  • Fit models
  • Overdispersion
  • Poisson-tweedie model
  • Zero-inflation


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