{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T10:48:05Z","timestamp":1767869285628,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Logistic regression models encounter challenges with correlated predictors and influential outliers. This study integrates robust estimators, including the Bianco\u2013Yohai 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.<\/jats:p>","DOI":"10.3390\/axioms14010019","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T07:34:19Z","timestamp":1735630459000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria\u2013Lukman Estimator"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2881-1297","authenticated-orcid":false,"given":"Adewale F.","family":"Lukman","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, University of North Dakota, Grand Forks, ND 58202, USA"}]},{"given":"Suleiman","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematical Sciences, African Institute for Mathematical Sciences, Mbour-Thies 23000, Senegal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4122-7061","authenticated-orcid":false,"given":"Olalekan","family":"Olaluwoye","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematical Sciences, African Institute for Mathematical Sciences, Mbour-Thies 23000, Senegal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0506-5984","authenticated-orcid":false,"given":"Rasha A.","family":"Farghali","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Insurance and Applied Statistics, Helwan University, Cairo 11795, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1002\/(SICI)1520-6750(199803)45:2<125::AID-NAV1>3.0.CO;2-A","article-title":"A robust regression technique using compound estimation","volume":"45","author":"Simpson","year":"1998","journal-title":"Nav. 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