Analyzing and identifying unusual observations in modified liu estimator using global influence technique

Jahufer, A; Alibuhtto, M C

dc.contributor.author	Jahufer, A
dc.contributor.author	Alibuhtto, M C
dc.date.accessioned	2017-01-02T05:31:53Z
dc.date.available	2017-01-02T05:31:53Z
dc.date.issued	12/29/2016
dc.identifier.citation	Proceedings of Fifth Annual Science Research Sessions 2016 on "Enriching the Novel Scientific Research for the Development of the Nation" pp.45-51	en_US
dc.identifier.isbn	9.78956E+12
dc.identifier.uri	http://ir.lib.seu.ac.lk/handle/123456789/2063
dc.description.abstract	Influence concepts have an important place in linear regression models and case deletion is a useful method for assessing the influence of single case. The influence measures in the presence of multicollinearity were discussed under the linear regression models when the errors structure is uncorrelated and homoscedastic. When modified Liu estimator (MLE) is used to mitigate the effects of multicollinearity, the influence of observations can be drastically modified. In this research paper it is aimed to analyze global influence techniques to detect influential observations in MLE. To illustrate the methodologies derived in this research paper a multicollinearity real data set was used to identify influential observations using global influence techniques derived in this research paper.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Faculty of Applied Sciences, South Eastern University of Sri lanka	en_US
dc.subject	Global Influence Measures	en_US
dc.subject	Leverages	en_US
dc.subject	Residuals	en_US
dc.subject	Modified Liu Estimator	en_US
dc.subject	Multicollinearity	en_US
dc.title	Analyzing and identifying unusual observations in modified liu estimator using global influence technique	en_US
dc.type	Article	en_US