Exponential dispersion models for overdispersed zero-inflated count data

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...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2023-07, Vol.52 (7), p.3286-3304
Main Authors: Bar-Lev, Shaul K., Ridder, Ad
Format: Article
Language:English
Subjects:
Count data analysis
Datasets
Dispersion
Exponential dispersion models
Fit models
Kurtosis
Overdispersion
Parameterization
Poisson-tweedie model
Statistical analysis
Statistical models
Zero-inflation
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Summary: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.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2021.1934020