Multiple linear regression model with stochastic design variables

Islam, M. Qamarul; Tiku, Moti L.

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