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Reparametrization of COM-Poisson Regression Models with Applications in the Analysis of Experimental Data by Ribeiro, Eduardo E.; Zeviani, Walmes M.; Bonat, Wagner H.; Demétrio, Clarice G. B.; Hinde, John is a scholarly article available to read on EtoBox.

What is Reparametrization of COM-Poisson Regression Models with Applications in the Analysis of Experimental Data about?

In the analysis of count data often the equidispersion assumption is not suitable, hence the Poisson regression model is inappropriate. As a generalization of the Poisson distribution, the COM-Poisson distribution can deal with under-, equi- and overdispersed count data. It is a member of the exponential family of distributions and has well known special cases. In spite of the nice properties of the COM-Poisson distribution, its location parameter does not correspond to the expectation, which complicates the interpretation of regression models. In this paper, we propose a straightforward reparametrization of the COM-Poisson distribution based on an approximation to the expectation of this distribution. The main advantage of our new parametrization is the straightforward interpretation of the regression coefficients in terms of the expectation, as usual in the context of generalized linear models. Furthermore, the estimation and inference for the new COM-Poisson regression model can be done based on the likelihood paradigm. We carried out simulation studies to verify the finite sample properties of the maximum likelihood estimators. The results from our simulation study show that th

Author

Ribeiro, Eduardo E.; Zeviani, Walmes M.; Bonat, Wagner H.; Demétrio, Clarice G. B.; Hinde, John

Published

2018

Language

EN