K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model
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,...
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/2/340 |
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| author | Adewale F. Lukman B. M. Golam Kibria Cosmas K. Nziku Muhammad Amin Emmanuel T. Adewuyi Rasha Farghali |
| author_facet | Adewale F. Lukman B. M. Golam Kibria Cosmas K. Nziku Muhammad Amin Emmanuel T. Adewuyi Rasha Farghali |
| author_sort | Adewale F. Lukman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:46:52Z |
| format | Article |
| id | doaj.art-c82407a2a43341a897cba1c70df66caa |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T11:46:52Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c82407a2a43341a897cba1c70df66caa2023-11-30T23:20:50ZengMDPI AGMathematics2227-73902023-01-0111234010.3390/math11020340K-L Estimator: Dealing with Multicollinearity in the Logistic Regression ModelAdewale F. Lukman0B. M. Golam Kibria1Cosmas K. Nziku2Muhammad Amin3Emmanuel T. Adewuyi4Rasha Farghali5Department of Epidemiology and Biostatistics, University of Medical Sciences, Ondo 220282, NigeriaDepartment of Mathematics and Statistics, Florida International University, Miami, FL 33199, USADepartment of Statistics, University of Dar es Salaam, Dar es Salaam 65015, TanzaniaDepartment of Statistics, University of Sargodha, Sargodha 40100, PakistanDepartment of Statistics, Ladoke Akintola University of Technology, Ogbomoso 210214, NigeriaDepartment of Mathematics, Insurance and Applied Statistics, Helwan University, Cairo 11732, EgyptMulticollinearity 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.https://www.mdpi.com/2227-7390/11/2/340Kibria-Lukman estimatorlogistic regression modelLiu estimatormulticollinearityridge regression estimator |
| spellingShingle | Adewale F. Lukman B. M. Golam Kibria Cosmas K. Nziku Muhammad Amin Emmanuel T. Adewuyi Rasha Farghali K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model Mathematics Kibria-Lukman estimator logistic regression model Liu estimator multicollinearity ridge regression estimator |
| title | K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model |
| title_full | K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model |
| title_fullStr | K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model |
| title_full_unstemmed | K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model |
| title_short | K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model |
| title_sort | k l estimator dealing with multicollinearity in the logistic regression model |
| topic | Kibria-Lukman estimator logistic regression model Liu estimator multicollinearity ridge regression estimator |
| url | https://www.mdpi.com/2227-7390/11/2/340 |
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