A simple and useful regression model for underdispersed count data based on Bernoulli–Poisson convolution

Marcelo Bourguignon (), Diego I. Gallardo and Rodrigo M. R. Medeiros
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Marcelo Bourguignon: Universidade Federal do Rio Grande do Norte
Diego I. Gallardo: Universidad de Atacama
Rodrigo M. R. Medeiros: Universidade de São Paulo

Statistical Papers, 2022, vol. 63, issue 3, No 5, 848 pages

Abstract: Abstract Count data is often modeled using Poisson regression, although this probability model naturally restricts the conditional variance to be equal to the conditional mean (equidispersion property). While overdispersion has been intensively studied, there are few alternative models in the statistical literature for analyzing count data with underdispersion. The primary goal of this paper is to introduce a novel model based on Bernoulli-Poisson convolution for modelling count data that are underdispersed relative to the Poisson distribution. We study the statistical properties of the proposed model, and we provide a useful interpretation of the parameters. We consider a regression structure for both components based on a new parameterization indexed by mean and dispersion parameters. An expectation-maximization (EM) algorithm is proposed for parameter estimation and some diagnostic measures, based on the EM algorithm, are considered. Simulation studies are conducted to evaluate its finite sample performance. Finally, we illustrate the usefulness of the new regression model by an application.

Keywords: Count data; EM-algorithm; Mean and dispersion models; Poisson distribution; Regression models; Underdispersion (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00362-021-01253-0

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