Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation

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dc.authoridSöküt Açar, Tuğba / 0000-0002-4444-1671
dc.contributor.authorSöküt Açar, Tuğba
dc.date.accessioned2025-01-27T19:37:14Z
dc.date.available2025-01-27T19:37:14Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical literature. The recently proposed Kibria-Lukman estimator is one of the Ridge-type estimators. The literature has compared the Kibria-Lukman estimator with the others using the mean square error criterion for the linear regression model. It was achieved in a study conducted on the Kibria-Lukman estimator's performance under the first-order autoregressive erroneous autocorrelation. When there is an autocorrelation problem with the second-order, evaluating the performance of the Kibria-Lukman estimator according to the mean square error criterion makes this paper original. The scalar mean square error of the Kibria-Lukman estimator under the second-order autoregressive error structure was evaluated using a Monte Carlo simulation and two real examples, and compared with the Generalized Least-squares, Ridge, and Liu estimators. The findings revealed that when the variance of the model was small, the mean square error of the Kibria-Lukman estimator gave very close values with the popular biased estimators. As the model variance grew, Kibria-Lukman did not give fairly similar values with popular biased estimators as in the model with small variance. However, according to the mean square error criterion the Kibria-Lukman estimator outperformed the Generalized Least-Squares estimator in all possible cases.
dc.identifier.doi10.53570/jnt.1139885
dc.identifier.endpage17
dc.identifier.issn2149-1402
dc.identifier.issue41
dc.identifier.startpage1
dc.identifier.trdizinid1154500
dc.identifier.urihttps://doi.org/10.53570/jnt.1139885
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1154500
dc.identifier.urihttps://hdl.handle.net/20.500.12428/17163
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofJournal of New Theory
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TRD_20250125
dc.subjectAutocorrelation
dc.subjectmulticollinearity
dc.subjectsecond-order autoregressive errors
dc.subjectKibria-Lukman estimator
dc.titleKibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation
dc.typeArticle

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