Detecting Influential observations in Two-Parameter Liu-Ridge Estimator

Influential observations do posed a major threat on the performance of regression model. Different influential statistics including Cook's Distance and DFFITS have been introduced in literatures using Ordinary Least Squares (OLS). The efficiency of these measures will be affected with the prese...

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Bibliographic Details
Published in:	Journal of Data Science Vol. 16; no. 2; pp. 207 - 217
Main Authors:	Lukman, Adewale F., Ayinde, Kayode
Format:	Journal Article
Language:	English
Published:	中華資料採礦協會 24-02-2021
Subjects:	Approximate Deletion Formulas Diagnostic Measures Influential Statistics Two-Parameter Liu-Ridge Estimator Multicollinearity
Online Access:	Get full text
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Summary:	Influential observations do posed a major threat on the performance of regression model. Different influential statistics including Cook's Distance and DFFITS have been introduced in literatures using Ordinary Least Squares (OLS). The efficiency of these measures will be affected with the presence of multicollinearity in linear regression. However, both problems can jointly exist in a regression model. New diagnostic measures based on the Two-Parameter Liu-Ridge Estimator (TPE) defined by Ozkale and Kaciranlar (2007) was proposed as alternatives to the existing ones. Approximate deletion formulas for the detection of influential cases for TPE are proposed. Finally, the diagnostic measures are illustrated with two real life dataset.
ISSN:	1683-8602 1680-743X 1683-8602
DOI:	10.6339/JDS.201804_16(2).0001