Abstract
The problem of multicollinearity, which occurs as a result of high correlation and linear dependence between two or more explanatory variables in a model, is one of the most important issues in model building, negatively affecting the parameter estimation process of the regression model. This study aims to review the shrinkage estimators used to address the problem of multicollinearity that appears in the zero-inflated Poisson regression model. This model is one of the most commonly used models when the response variable data have countable values and are not normally distributed. Through Monte Carlo simulation experiments, it was found that the shrinkage estimator with two parameters is the best proposed estimator due to its reduction of the model's mean square error.
