Transformation and weighting in regression
Transformation and weighting in regression
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
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Excess zeros and overdispersion are common phenomena that limit the use of traditional Poisson regression models for modeling count data. Both excess zeros and overdispersion caused by unobserved heterogeneity are accounted for by the proposed zero-inflated Poisson (ZIP) regression mixture model. To estimate the parameters of the model, an EM algorithm with an embedded iteratively reweighted least squares method is implemented. The parameter estimation performance of the proposed model is evaluated through simulation studies. The ZIP regression mixture model is applied to the DMFT index dataset, which contains excess zeros and overdispersion. Comparisons of several other models commonly used for such data with the ZIP regression mixture model show that, in general, the latter model fits the data well.