New Two-Parameter Estimators for the Logistic Regression Model with Multicollinearity
Article
Awwad, FA, Odeniyi, KA, Dawoud, I et al. (2022). New Two-Parameter Estimators for the Logistic Regression Model with Multicollinearity
. 21 403-414. 10.37394/23206.2022.21.48
Awwad, FA, Odeniyi, KA, Dawoud, I et al. (2022). New Two-Parameter Estimators for the Logistic Regression Model with Multicollinearity
. 21 403-414. 10.37394/23206.2022.21.48
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.
