Poisson mixture distribution analysis for North Carolina SIDS counts using information criteria
Mixture distribution analysis provides us with a tool for identifying unlabeled clusters that naturally arise in a data set. In this paper, we demonstrate how to use the information criteria AIC and BIC to choose the optimal number of clusters for a given set of univariate Poisson data. We give an...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Milano University Press
2017-09-01
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| Series: | Epidemiology, Biostatistics and Public Health |
| Online Access: | http://ebph.it/article/view/12550 |
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