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Multiple linear regression model with stochastic design variables

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dc.contributor.author Islam, M. Qamarul
dc.contributor.author Tiku, Moti L.
dc.date.accessioned 2016-06-13T11:16:32Z
dc.date.available 2016-06-13T11:16:32Z
dc.date.issued 2010
dc.identifier.citation Islam, M.Q., Tiku, M.L. (2010). Multiple linear regression model with stochastic design variables. Journal of Applied Statistics, 37(6), 923-943. http://dx.doi.org/10.1080/02664760902939612 tr_TR
dc.identifier.issn 0266-4763
dc.identifier.uri http://hdl.handle.net/20.500.12416/1086
dc.description.abstract In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given tr_TR
dc.language.iso eng tr_TR
dc.publisher Routledge Journals tr_TR
dc.relation.isversionof 10.1080/02664760902939612 tr_TR
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Correlation Coefficient tr_TR
dc.subject Least Squares tr_TR
dc.subject Linear Regression tr_TR
dc.subject Modified Maximum Likelihood tr_TR
dc.subject Multivariate Distributions tr_TR
dc.subject Non-Normality tr_TR
dc.subject Random Design tr_TR
dc.title Multiple linear regression model with stochastic design variables tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Applied Statistics tr_TR
dc.identifier.volume 37 tr_TR
dc.identifier.issue 6 tr_TR
dc.identifier.startpage 923 tr_TR
dc.identifier.endpage 943 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi İdari Bilimler Fakültesi, İktisat Bölümü tr_TR


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