Publication:
On the performance of the Jackknifed Liu-type estimator in linear regression model

dc.contributor.authorYILDIZ, NİLGÜN
dc.contributor.authorsYildiz, Nilgun
dc.date.accessioned2022-03-12T22:25:04Z
dc.date.accessioned2026-01-11T11:35:34Z
dc.date.available2022-03-12T22:25:04Z
dc.date.issued2018
dc.description.abstractIn this paper, we are proposing a modified jackknife Liu-type estimator (MJLTE) that was created by combining the ideas underlying both the Liu-type estimator (LTE) and the jackknifed Liu-type estimator (JLTE). We will also present the necessary and sufficient conditions for superiority of the MJLTE over the LTE and JLTE, in terms of mean square error matrix criterion. Finally, a real data example and a Monte Carlo simulation are also given to illustrate theoretical results.
dc.identifier.doi10.1080/03610926.2017.1339087
dc.identifier.eissn1532-415X
dc.identifier.issn0361-0926
dc.identifier.urihttps://hdl.handle.net/11424/234871
dc.identifier.wosWOS:000424774600018
dc.language.isoeng
dc.publisherTAYLOR & FRANCIS INC
dc.relation.ispartofCOMMUNICATIONS IN STATISTICS-THEORY AND METHODS
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLiu-type estimators
dc.subjectJackknifed estimator
dc.subjectJackknifed Liu-type estimator
dc.subjectMean squared error matrix
dc.subjectRIDGE-REGRESSION
dc.subjectBIAS
dc.titleOn the performance of the Jackknifed Liu-type estimator in linear regression model
dc.typearticle
dspace.entity.typePublication
oaire.citation.endPage2290
oaire.citation.issue9
oaire.citation.startPage2278
oaire.citation.titleCOMMUNICATIONS IN STATISTICS-THEORY AND METHODS
oaire.citation.volume47

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