Detecting global influential observations in liu regression model

Jahufer, Aboobacker

dc.contributor.author	Jahufer, Aboobacker
dc.date.accessioned	2017-02-14T07:08:36Z
dc.date.available	2017-02-14T07:08:36Z
dc.date.issued	2013-02
dc.identifier.citation	Open Journal of Statistics pp. 5-11	en_US
dc.identifier.uri	http://ir.lib.seu.ac.lk/handle/123456789/2350
dc.description.abstract	In linear regression analysis, detecting anomalous observations is an important step for model building process. Various influential measures based on different motivational arguments and designed to measure the influence of observations on different aspects of various regression results are elucidated and critiqued. The presence of influential observations in the data is complicated by the presence of multicollinearity. In this paper, when Liu estimator is used to mitigate the effects of multicollinearity the influence of some observations can be drastically modified. Approximate deletion for- mulas for the detection of influential points are proposed for Liu estimator. Two real macroeconomic data sets are used to illustrate the methodologies proposed in this paper	en_US
dc.language.iso	en	en_US
dc.publisher	Open Journal of Statistics	en_US
dc.subject	Liu estimator	en_US
dc.subject	Global Influential Observations	en_US
dc.subject	Diagnostics	en_US
dc.subject	Multicollinearity	en_US
dc.subject	Case deletion	en_US
dc.subject	Approximate deletion formulas	en_US
dc.title	Detecting global influential observations in liu regression model	en_US
dc.type	Article	en_US