Reparametrization of COM-Poisson Regression Models with Applications in the Analysis of Experimental Data

TL;DR

This paper introduces a new reparametrization of the COM-Poisson regression model that simplifies interpretation and allows for effective analysis of over-, under-, and equidispersed count data, with verified unbiased estimators.

Contribution

It proposes a reparametrization based on the expectation approximation, enabling straightforward interpretation of regression coefficients in COM-Poisson models.

Findings

MLE estimators are unbiased and consistent.

Regression and dispersion parameters are nearly orthogonal.

Model effectively handles various dispersion levels in count data.

Abstract

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 the maximum likelihood estimators are unbiased and consistent for both regression and dispersion parameters. We observed that the empirical correlation between the regression and dispersion parameter estimators is close to zero, which suggests that these parameters are orthogonal. We illustrate the application of the proposed model through the analysis of three data sets with over-, under- and equidispersed count data. The study of distribution properties through a consideration of dispersion, zero-inflated and heavy tail indexes, together with the results of data analysis show the flexibility over standard approaches.