Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator
Logistic regression models encounter challenges with correlated predictors and influential outliers. This study integrates robust estimators, including the Bianco–Yohai estimator (BY) and conditionally unbiased bounded influence estimator (CE), with the logistic Liu (LL), logistic ridge (LR), and lo...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2075-1680/14/1/19 |
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| author | Adewale F. Lukman Suleiman Mohammed Olalekan Olaluwoye Rasha A. Farghali |
| author_facet | Adewale F. Lukman Suleiman Mohammed Olalekan Olaluwoye Rasha A. Farghali |
| author_sort | Adewale F. Lukman |
| collection | DOAJ |
| description | Logistic regression models encounter challenges with correlated predictors and influential outliers. This study integrates robust estimators, including the Bianco–Yohai estimator (BY) and conditionally unbiased bounded influence estimator (CE), with the logistic Liu (LL), logistic ridge (LR), and logistic KL (KL) estimators. The resulting estimators (LL-BY, LL-CE, LR-BY, LR-CE, KL-BY, and KL-CE) are evaluated through simulations and real-life examples. KL-BY emerges as the preferred choice, displaying superior performance by reducing mean squared error (MSE) values and exhibiting robustness against multicollinearity and outliers. Adopting KL-BY can lead to stable and accurate predictions in logistic regression analysis. |
| format | Article |
| id | doaj-art-8b5927f5fa6841e5b086aad2174a73e8 |
| institution | Kabale University |
| issn | 2075-1680 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-8b5927f5fa6841e5b086aad2174a73e82025-01-24T13:22:10ZengMDPI AGAxioms2075-16802024-12-011411910.3390/axioms14010019Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman EstimatorAdewale F. Lukman0Suleiman Mohammed1Olalekan Olaluwoye2Rasha A. Farghali3Department of Mathematics and Statistics, University of North Dakota, Grand Forks, ND 58202, USADepartment of Applied Mathematical Sciences, African Institute for Mathematical Sciences, Mbour-Thies 23000, SenegalDepartment of Applied Mathematical Sciences, African Institute for Mathematical Sciences, Mbour-Thies 23000, SenegalDepartment of Mathematics, Insurance and Applied Statistics, Helwan University, Cairo 11795, EgyptLogistic regression models encounter challenges with correlated predictors and influential outliers. This study integrates robust estimators, including the Bianco–Yohai estimator (BY) and conditionally unbiased bounded influence estimator (CE), with the logistic Liu (LL), logistic ridge (LR), and logistic KL (KL) estimators. The resulting estimators (LL-BY, LL-CE, LR-BY, LR-CE, KL-BY, and KL-CE) are evaluated through simulations and real-life examples. KL-BY emerges as the preferred choice, displaying superior performance by reducing mean squared error (MSE) values and exhibiting robustness against multicollinearity and outliers. Adopting KL-BY can lead to stable and accurate predictions in logistic regression analysis.https://www.mdpi.com/2075-1680/14/1/19logistic regressionoutliersmulticollinearityrobust estimatorsBianco–Yohai estimatorridge regression estimator |
| spellingShingle | Adewale F. Lukman Suleiman Mohammed Olalekan Olaluwoye Rasha A. Farghali Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator Axioms logistic regression outliers multicollinearity robust estimators Bianco–Yohai estimator ridge regression estimator |
| title | Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator |
| title_full | Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator |
| title_fullStr | Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator |
| title_full_unstemmed | Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator |
| title_short | Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator |
| title_sort | handling multicollinearity and outliers in logistic regression using the robust kibria lukman estimator |
| topic | logistic regression outliers multicollinearity robust estimators Bianco–Yohai estimator ridge regression estimator |
| url | https://www.mdpi.com/2075-1680/14/1/19 |
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