Logistic regression, a widely utilized regression model for binary response variables, relies on the maximum likelihood method for parameter estimation. However, when multicollinearity exists among independent variables, the estimators become ineffective due to variance inflation. To address this issue, various methods, including ridge regression, have been proposed. Ridge regression is crucial in estimating the ridge adjustment parameter, and several formulas have been suggested for this purpose. This thesis aims to introduce and compare a comprehensive set of ridge parameter formulas for logistic regression, utilizing efficiency criteria. To achieve this objective, Monte Carlo simulations will be conducted by varying correlation, the number of predictor variables, and sample size. The performance of selected ridge estimators will be compared, and the most suitable ones will be identified and recommended. Furthermore, the introduced ridge estimators, with different parameters, will be applied to real-world examples in the field of medical sciences. The implementation of the research utilizes R software, and the codes employed are presented in a dedicated section, ensuring practicality and accessibility.
