Bayesian Conway–Maxwell–Poisson regression models for overdispersed and underdispersed counts
- Publisher:
- Informa UK Limited
- Publication Type:
- Journal Article
- Citation:
- Communications in Statistics - Theory and Methods, 2020, pp. 1-12
- Issue Date:
- 2020
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Bayesian Conway Maxwell Poisson regression models for overdispersed and underdispersed counts.pdf | Accepted version | 10.06 MB |
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© 2019, © 2019 Taylor & Francis Group, LLC. Bayesian models that can handle both overdispersed and underdispersed counts are rare in the literature, perhaps because full probability distributions for dispersed counts are rather difficult to construct. This note takes a first look at Bayesian Conway–Maxwell–Poisson regression models that can handle both overdispersion and underdispersion yet retain the parsimony and interpretability of classical count models. The focus is on providing an explicit demonstration of Bayesian regression inferences for dispersed counts via a Metropolis–Hastings algorithm. We illustrate the approach on two data analysis examples and demonstrate some favorable frequentist properties via a simulation study.
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