A new Jackknifing ridge estimator for logistic regression model

Document Type : Research Paper


1 Department of management Information systems, College of Administration and Economics, University of Mosul, Mosul, Iraq.

2 Department of Statistics and Informatics, University of Mosul, Mosul, Iraq.


In reducing the effects of collinearity, the ridge estimator (RE) has been consistently demonstrated to be an attractive shrinkage method. In application, when the response variable is binary data, the logistic regression model (LRM) is a well-known model. However, it is known that collinearity negatively affects the variance of maximum likelihood estimator of the LRM. To address this problem, a logistic ridge estimator was proposed by several authors. In this work, a Jackknifing logistic ridge estimator (NJLRE) is proposed and derived. The Monte Carlo simulation results recommend that the NJLRE estimator can bring significant improvement relative to other existing estimators. Furthermore, the real application results demonstrate that the NJLRE estimator outperforms both LRE and MLE in terms of predictive performance. 


Volume 13, Issue 1
February 2022
Pages 2127-2135
  • Receive Date: 05 December 2021
  • Accept Date: 05 December 2021
  • First Publish Date: 05 December 2021