A more interpretable regression model for count data with excess of zeros
Methodology
2025-09-30 v1
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.
Cite
@article{arxiv.2509.24916,
title = {A more interpretable regression model for count data with excess of zeros},
author = {Gustavo H. A. Pereira and Jeremias Leão and Manoel Santos-Neto and Jianwen Cai},
journal= {arXiv preprint arXiv:2509.24916},
year = {2025}
}