A new biased estimation method in tobit regression: theory and application

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dc.contributor.authorAsar, Yasin
dc.contributor.authorOgutcuoglu, Esra
dc.date.accessioned2024-02-23T14:17:12Z
dc.date.available2024-02-23T14:17:12Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractIn this study, the effects of multicollinearity on the maximum likelihood estimator are analyzed in the tobit regression model. It is known that the near-linear dependencies in the design matrix affect the maximum likelihood estimation negatively, namely, the standard errors become so large so that the estimations are said to be inconsistent. Therefore, a new biased estimator being a generalization of the well-known Liu estimator is introduced as an alternative to the maximum likelihood estimator. Mean squared error properties of the estimators are investigated theoretically. In order to evaluate the performances of the estimators, a Monte Carlo simulation study is designed and simulated mean squared error is used as a performance criterion. Finally, the benefits of the new estimator is illustrated via real data applications.en_US
dc.identifier.doi10.1080/00949655.2020.1845699
dc.identifier.endpage1273en_US
dc.identifier.issn0094-9655
dc.identifier.issn1563-5163
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85095748420en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1257en_US
dc.identifier.urihttps://doi.org/10.1080/00949655.2020.1845699
dc.identifier.urihttps://hdl.handle.net/20.500.12452/12992
dc.identifier.volume91en_US
dc.identifier.wosWOS:000587877700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal Of Statistical Computation And Simulationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulticollinearityen_US
dc.subjectLiu Estimatoren_US
dc.subjectTobit Modelen_US
dc.subjectMonte Carlo Simulationen_US
dc.subjectMean Squared Erroren_US
dc.titleA new biased estimation method in tobit regression: theory and applicationen_US
dc.typeArticleen_US

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