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Three-level zero-inflated Conway–Maxwell–Poisson regression model for analyzing dispersed clustered count data with extra zeros

Somayeh Ghorbani Gholiabad, Abbas Moghimbeigi () and Javad Faradmal
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Somayeh Ghorbani Gholiabad: Hamadan University of Medical Sciences
Abbas Moghimbeigi: Alborz University of Medical Sciences
Javad Faradmal: Hamadan University of Medical Sciences

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 2, No 10, 415-439

Abstract: Abstract The count response variables are usually included of extra zeros. A useful tool for modeling such data is zero-inflated regression models. In the last decade, the Conway–Maxwell–Poisson model is applied for analyzing count data that can handle under- and over-dispersed data, besides that can encompass the Poisson and negative binomial. Sometimes, due to the sampling design or the data collection procedure, the data simultaneously are clustered or correlated with extra zeros and under- or over-dispersion. We applied a three-level zero-inflated Conway–Maxwell–Poisson regression model to overcome these problems. An expectation-maximization algorithm is used to estimate the model parameters of an appropriate penalized log-likelihood function. Model flexibility and finite-sample properties of this methodology have been investigated by extensive simulation study. The method has been illustrated with an application on real data in the health survey. Furthermore, we compared the results of the model with a three-level zero-inflated negative binomial regression model.

Keywords: Clustered count data; Mixed effect model; Multilevel data; Over-dispersion; Zero-inflated Conway–Maxwell–Poisson model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13571-020-00229-8

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