This paper investigates the use of shrinkage estimators in the generalized Poisson hurdle (GPH) model for count data analysis. The GPH model effectively handles data with both excess zeros and over‐ or underdispersion. We propose shrinkage estimators to improve parameter estimation in this model and analyze their asymptotic properties, including biases and risks. An extensive comparison through Monte Carlo simulations evaluates the efficacy of the suggested estimators against the maximum likelihood estimator, employing a simulated relative efficiency criterion. In addition, we apply the estimators to two real‐world datasets. Our findings illustrate that the suggested shrinkage estimators yield superior results compared to the traditional maximum likelihood estimator.
