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Comparing of the Maximum Likelihood (Ml) and the Least Squares (Ls) Methods in Terms of Variance Components for Unequal Numbers of Abservations in Subclasses

dc.authorscopusid 59577148200
dc.authorscopusid 59026085600
dc.contributor.author Orhan, H.
dc.contributor.author Okut, H.
dc.date.accessioned 2025-05-10T17:05:11Z
dc.date.available 2025-05-10T17:05:11Z
dc.date.issued 1996
dc.department T.C. Van Yüzüncü Yıl Üniversitesi en_US
dc.department-temp Orhan H., Kahramanmaras Sutcu Imam U., Ziraat Fakültesi, Kahramanmaraş, Türkiye; Okut H., Yuzuncu Yil Universitesi, Ziraat Fakültesi, Van, Türkiye en_US
dc.description.abstract In this study, two parameter estimators. Maximum Likelihood (ML) and Least Squere (LS) methods, have been compared in case of random and mixed model conditions with respect to the efficiency of the estimated parameters. According to results obtained, ML method should be preferred to LS method in the case of random and mixed models for unequal numbers of observation in subclasses. en_US
dc.identifier.doi 10.55730/1300-0128.4104
dc.identifier.endpage 297 en_US
dc.identifier.issn 1300-0128
dc.identifier.issue 4 en_US
dc.identifier.scopus 2-s2.0-0030536238
dc.identifier.scopusquality Q3
dc.identifier.startpage 293 en_US
dc.identifier.uri https://doi.org/10.55730/1300-0128.4104
dc.identifier.uri https://hdl.handle.net/20.500.14720/6235
dc.identifier.volume 20 en_US
dc.identifier.wosquality Q4
dc.language.iso tr en_US
dc.publisher Scientific and Technical Research Council of Turkey en_US
dc.relation.ispartof Turkish Journal of Veterinary and Animal Sciences en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Least Square (Ls) Methods en_US
dc.subject Maxsimum Likelihood (Ml) en_US
dc.subject Variance Components en_US
dc.title Comparing of the Maximum Likelihood (Ml) and the Least Squares (Ls) Methods in Terms of Variance Components for Unequal Numbers of Abservations in Subclasses en_US
dc.type Article en_US

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