Multiple linear regression model under nonnormality

Islam, M. Qamarul; Tiku, Moti L.

dc.contributor.author	Islam, M. Qamarul
dc.contributor.author	Tiku, Moti L.
dc.date.accessioned	2024-04-25T07:36:48Z
dc.date.available	2024-04-25T07:36:48Z
dc.date.issued	2004-10
dc.identifier.citation	Islam, M. Qamarul; Tiku, Moti 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	03610926
dc.identifier.uri	http://hdl.handle.net/20.500.12416/7959
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	Hypothesis Testing	tr_TR
dc.subject	Least Squares	tr_TR
dc.subject	M Estimators	tr_TR
dc.subject	Modified Likelihood	tr_TR
dc.subject	Multiple Linear Regression	tr_TR
dc.subject	Nonnormality	tr_TR
dc.subject	Outliers	tr_TR
dc.subject	Robustness	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, İktisat Bölümü	tr_TR