A simulation-based study of ZIP regression with various zero-inflated submodels
Essoham Ali · Communications in Statistics - Simulation and Computation · 2022
In this paper, we are interested in the robustness of the estimation in the Zero-Inflated Poisson regression model, when varying the class membership model of the underlying mixture. We propose an estimation procedure based on the maximum likelihood estimator. Simulations are used to examine the performance of the MLE. The results suggest that maximum likelihood allows for accurate inference. Using simulated datasets, we show that the proposed alternative link functions are quite flexible and outperform the standard link function. Also, an application to a real dataset is provided.