Addressing multicollinearity in general linear model: A novel approach for ridge parameter with performance comparison

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dc.contributor.authorLuqman, Muhammad
dc.contributor.authorBhatti, Sajjad Haider
dc.contributor.authorAydin, Demet
dc.contributor.authorJamil, Mohsin
dc.date.accessioned2026-04-25T14:20:19Z
dc.date.available2026-04-25T14:20:19Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractThe problem of ill-conditioned data or multicollinearity is common in regression modelling. The problem results in imprecise parameter estimation which leads to inability of gauging true impact of explanatory variables on the response. Also, due to strong multicollinearity, standard errors of parameter estimates get inflated leading to wider confidence intervals and hence increased risk of type-II error. To handle the problem, different approaches have been proposed in literature. Primarily, such techniques penalize the coefficient estimates in one way or other. Ridge regression is one of the most applied among such techniques. In ridge regression, a penalty term is added in the objective function of the general linear model. That penalty term introduces a small amount of bias in parameter estimates with an objective to decrease the mean square error. In the current article, some new choices for ridge constant are proposed. The performance of proposed ridge choices are compared through Monte Carlo simulations under different scenarios, using mean square error as measure of performance. The simulation results indicate that the proposed ridge estimator performs better than existing ridge constants, in most cases catering for severity of multicollinearity, number of explanatory variables, sample size and error variance structure. The simulation results were further corroborated by comparing performance of proposed ridge penalties using two real-life applications.
dc.identifier.doi10.1371/journal.pone.0335072
dc.identifier.issn1932-6203
dc.identifier.issue10
dc.identifier.orcid0000-0002-3491-8392
dc.identifier.orcid0000-0002-8835-2451
dc.identifier.orcid0000-0002-3206-4565
dc.identifier.pmid41134860
dc.identifier.scopus2-s2.0-105019647585
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0335072
dc.identifier.urihttps://hdl.handle.net/11486/8503
dc.identifier.volume20
dc.identifier.wosWOS:001600885400002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPublic Library Science
dc.relation.ispartofPlos One
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subject#BAŞV!
dc.titleAddressing multicollinearity in general linear model: A novel approach for ridge parameter with performance comparison
dc.typeArticle

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