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http://bura.brunel.ac.uk/handle/2438/15877
Title: | A Simple and Adaptive Dispersion Regression Model for Count Data |
Authors: | Yu, K Vinciotti, V Klakattawi, H |
Issue Date: | 2018 |
Citation: | Entropy |
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. |
URI: | http://bura.brunel.ac.uk/handle/2438/15877 |
ISSN: | 1099-4300 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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