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http://bura.brunel.ac.uk/handle/2438/15877
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DC Field | Value | Language |
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dc.contributor.author | Yu, K | - |
dc.contributor.author | Vinciotti, V | - |
dc.contributor.author | Klakattawi, H | - |
dc.date.accessioned | 2018-02-28T09:43:44Z | - |
dc.date.available | 2018-02-28T09:43:44Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Entropy | en_US |
dc.identifier.issn | 1099-4300 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/15877 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.title | A Simple and Adaptive Dispersion Regression Model for Count Data | en_US |
dc.type | Article | en_US |
dc.relation.isPartOf | Entropy | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | 349.19 kB | Adobe PDF | View/Open |
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