A Simulation Study of Some Logistic, Poisson, and Multiple Ridge Regression Estimators

This paper is a Monte Carlo simulation study of some logistic, Poisson, and multiple ridge regression estimators. This study proposes new ridge regression estimators using linear combinations of known ridge parameters, developed through grid search and methods that leverage mean squared error (MSE) values from prior simulations. The performance of each known and proposed estimators are then compared using MSE criterion. Results show that the proposed estimators performed better on many cases. Furthermore, each estimator was applied to secondary data and was compared based on their respective estimated coefficients.