A New Ridge-Type Estimator in the Zero-Inflated Bell Regression Model

Count data modeling requires usage of the Poisson regression model as a primary analytic method. Excess dispersion in variables makes the model unfit to use when the Poisson distribution mean value differs from its variance value. Data fits well with the results obtained by using the Bell regression model. Excess zeros occur frequently in the observed count data records. The Zero-Inflated Bell regression model is a substitute for the Bell regression model in this situation. The approach of maximum likelihood is mostly used to estimate the Zero-Inflated Bell regression model's parameters. When modeling the link between the response variable and two or more explanatory variables in an extended linear model, such as the Zero-Inflated Bell regression model, linear dependency poses a risk in a real-life application. It decreased the greatest likelihood estimator's effectiveness. To address this problem, we proposed a new ridge estimator for the Zero-Inflated Bell regression model. The results of the simulations and implementations validate the suggested approaches' superiority to the traditional maximum likelihood estimator.