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- Title
A Simple and Adaptive Dispersion Regression Model for Count Data.
- Authors
Klakattawi, Hadeel S.; Vinciotti, Veronica; Yu, Keming
- Abstract
Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can be easily implemented in practice. In this paper, a discreteWeibull regression model is shown to be able to adapt in a simple way to different types of dispersions relative to Poisson regression: overdispersion, underdispersion and covariate-specific dispersion. Maximum likelihood can be used for efficient parameter estimation. The description of the model, parameter inference and model diagnostics is accompanied by simulated and real data analyses.
- Subjects
WEIBULL distribution; DISCRETE systems; PARTICLE size determination; LINEAR statistical models; DATA analysis; REGRESSION analysis
- Publication
Entropy, 2018, Vol 20, Issue 2, p142
- ISSN
1099-4300
- Publication type
Article
- DOI
10.3390/e20020142