New Two-Parameter Estimators for the Logistic Regression Model with Multicollinearity
Fuad A. Awwad, Kehinde Odeniyi, İssam Dawoud, Zakariya Yahya Algamal, Mohamed R. Abonazel, B. M. Golam Kibria, Elsayed Tag Eldin
Abstract
We proposed new two-parameter estimators to solve the problem called multicollinearity for the logistic regression model in this paper. We have derived these estimators’ properties and using the mean squared error (MSE) criterion; we compare theoretically with some of existing estimators, namely the maximum likelihood, ridge, Liu estimator, Kibria-Lukman, and Huang estimators. Furthermore, we obtain the estimators for k and d. A simulation is conducted in order to compare the estimators' performances. For illustration purposes, two real-life applications have been analyzed, that supported both theoretical and a simulation. We found that the proposed estimator, which combines the Liu estimator and the Kibria-Lukman estimator, has the best performance.