Detecting influential observations in Liu and modified Liu estimators

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dc.authorid0000-0002-4589-1383en_US
dc.authorid0000-0003-0678-7935en_US
dc.contributor.authorErtaş, Hasan
dc.contributor.authorErişoğlu, Murat
dc.contributor.authorKaçıranlar, Selahattin
dc.date.accessioned2021-12-06T06:54:41Z
dc.date.available2021-12-06T06:54:41Z
dc.date.issued2013
dc.departmentAÇÜ, Orman Fakültesi, Orman Endüstri Mühendisliği Bölümüen_US
dc.description.abstractIn regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. A numerical example using a real data set used by Longley [10] and a Monte Carlo simulation are given to illustrate the theoretical results.
dc.identifier.citationErtaş, H., Erişoğlu, M., & Kaçıranlar, S. (2013). Detecting influential observations in Liu and modified Liu estimators. Journal of Applied Statistics, 40(8), 1735-1745.en_US
dc.identifier.doi10.1080/02664763.2013.794203
dc.identifier.endpage1745en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage1735en_US
dc.identifier.urihttps://hdl.handle.net/11494/3585
dc.identifier.volume40en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorErtaş, Hasan
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofJournal of Applied Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInfluential observationsen_US
dc.subjectDiagnosticsen_US
dc.subjectMulticollinearityen_US
dc.subjectLiu estimatoren_US
dc.subjectModified Liu estimatoren_US
dc.titleDetecting influential observations in Liu and modified Liu estimatorsen_US
dc.typeArticle

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