K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model

Adewale F. Lukman (), B. M. Golam Kibria, Cosmas K. Nziku, Muhammad Amin, Emmanuel T. Adewuyi and Rasha Farghali
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Adewale F. Lukman: Department of Epidemiology and Biostatistics, University of Medical Sciences, Ondo 220282, Nigeria
B. M. Golam Kibria: Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA
Cosmas K. Nziku: Department of Statistics, University of Dar es Salaam, Dar es Salaam 65015, Tanzania
Muhammad Amin: Department of Statistics, University of Sargodha, Sargodha 40100, Pakistan
Emmanuel T. Adewuyi: Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso 210214, Nigeria
Rasha Farghali: Department of Mathematics, Insurance and Applied Statistics, Helwan University, Cairo 11732, Egypt

Mathematics, 2023, vol. 11, issue 2, 1-14

Abstract: Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in both the linear and generalized linear models. The Kibria and Lukman estimator (KLE) was developed as an alternative to the MLE to handle multicollinearity for the linear regression model. In this study, we proposed the Logistic Kibria-Lukman estimator (LKLE) to handle multicollinearity for the logistic regression model. We theoretically established the superiority condition of this new estimator over the MLE, the logistic ridge estimator (LRE), the logistic Liu estimator (LLE), the logistic Liu-type estimator (LLTE) and the logistic two-parameter estimator (LTPE) using the mean squared error criteria. The theoretical conditions were validated using a real-life dataset, and the results showed that the conditions were satisfied. Finally, a simulation and the real-life results showed that the new estimator outperformed the other considered estimators. However, the performance of the estimators was contingent on the adopted shrinkage parameter estimators.

Keywords: Kibria-Lukman estimator; logistic regression model; Liu estimator; multicollinearity; ridge regression estimator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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