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Multiple linear regression model under nonnormality

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dc.contributor.author Islam, M. Q.
dc.contributor.author Tiku, M. L.
dc.date.accessioned 2021-12-14T10:39:11Z
dc.date.available 2021-12-14T10:39:11Z
dc.date.issued 2004-10
dc.identifier.citation Islam, M. Q.; Tiku, M. L. (2004). "Multiple linear regression model under nonnormality", Communications in Statistics-Theory and Methods, Vol. 33, No. 10, pp. 2443-2467 tr_TR
dc.identifier.issn 0361-0926
dc.identifier.uri http://hdl.handle.net/20.500.12416/4962
dc.description.abstract We consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1081/STA-200031519 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Multiple Linear Regression tr_TR
dc.subject Modified Likelihood tr_TR
dc.subject Robustness tr_TR
dc.subject M Estimators tr_TR
dc.subject Least Squares tr_TR
dc.subject Nonnormality tr_TR
dc.subject Hypothesis Testing tr_TR
dc.subject Outliers tr_TR
dc.title Multiple linear regression model under nonnormality tr_TR
dc.type article tr_TR
dc.relation.journal Communications in Statistics-Theory and Methods tr_TR
dc.identifier.volume 33 tr_TR
dc.identifier.issue 10 tr_TR
dc.identifier.startpage 2443 tr_TR
dc.identifier.endpage 2467 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, Ekonomi Bölümü tr_TR


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