A more interpretable regression model for count data with excess of zeros

Gustavo H. A. Pereira; Jeremias Leão; Manoel Santos-Neto; Jianwen Cai

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arXiv · 2509.24916

A more interpretable regression model for count data with excess of zeros

Gustavo H. A. Pereira·Jeremias Leão·Manoel Santos-Neto·Jianwen Cai

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Abstract

Count data are common in medical research. When these data have more zeros than expected by the most used count distributions, it is common to employ a zero-inflated regression model. However, the interpretability of these models is much lower than the most used count regression models. In this work, we introduce a more interpretable regression model for count data with excess of zeros based on a reparameterization of the zero-inflated Poisson distribution. We discuss inferential and diagnostic tools and perform a Monte Carlo simulation study to evaluate the performance of the maximum likelihood estimator. Finally, the usefulness of the proposed regression model is illustrated through an application on children mortality.

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Gustavo H. A. Pereira, Jeremias Leão, Manoel Santos-Neto, Jianwen Cai. 2025-09-29. A more interpretable regression model for count data with excess of zeros. https://arxiv.org/abs/2509.24916

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