The modified maximum likelihood regression type estimators using bivariate ranked set sampling

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dc.contributor.authorSazak, Hakan Savas
dc.contributor.authorZeybek, Melis
dc.date.accessioned2019-10-27T09:43:34Z
dc.date.available2019-10-27T09:43:34Z
dc.date.issued2019
dc.departmentEge Üniversitesien_US
dc.description.abstractRank set sampling (RSS) is an efficient sampling technique, used especially in the situations where the measurement on the variable of interest is time-consuming, costly or difficult. One of the modifications to make it more efficient is the regression type estimation using bivariate RSS. We derive the modified maximum likelihood (MML) regression type estimators using bivariate RSS when the concomitant variable X is stochastic and the error term is non-normal where it is problematic to obtain the maximum likelihood (ML) estimators. The procedures and merits of the proposed estimators are illustrated through simulations and two real-life applications.en_US
dc.identifier.doi10.1080/03610918.2019.1628272
dc.identifier.issn0361-0918
dc.identifier.issn1532-4141
dc.identifier.issn0361-0918en_US
dc.identifier.issn1532-4141en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1080/03610918.2019.1628272
dc.identifier.urihttps://hdl.handle.net/11454/28883
dc.identifier.wosWOS:000475241000001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofCommunications in Statistics-Simulation and Computationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBivariate ranked set samplingen_US
dc.subjectconcomitant variableen_US
dc.subjectmodified maximum likelihooden_US
dc.subjectregression type estimationen_US
dc.subjectthree-parameter Weibull distributionen_US
dc.titleThe modified maximum likelihood regression type estimators using bivariate ranked set samplingen_US
dc.typeArticleen_US

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