Combining modified ridge-type and principal component regression estimators

Combining modified ridge-type and principal component regression estimators

The performance of ordinary least squares estimator (OLSE) when there is multicollinearity (MC) in a linear regression model becomes inefficient. The principal components regression and the modified ridge-type estimator have been proposed at a different time to handle the problem of MC. However, in...

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Bibliographic Details
Published in:Scientific African Vol. 9; p. e00536
Main Authors: Lukman, Adewale F., Ayinde, Kayode, Oludoun, Olajumoke, Onate, Clement A.
Format: Journal Article
Language:English
Published: Elsevier B.V 01-09-2020
Elsevier
Subjects:
Competing estimators
Modified ridge-type
OLSE
Principal component
Principal component
OLSE
Modified ridge-type
Competing estimators
Online Access:Get full text
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Summary:The performance of ordinary least squares estimator (OLSE) when there is multicollinearity (MC) in a linear regression model becomes inefficient. The principal components regression and the modified ridge-type estimator have been proposed at a different time to handle the problem of MC. However, in this paper, we developed a new estimator by combining these two estimators and derived the necessary and sufficient condition for its superiority over other competing estimators. Furthermore, we establish the dominance of this new estimator over other estimators through a simulation study, and numerical example in terms of the estimated mean squared error.
ISSN:2468-2276
2468-2276
DOI:10.1016/j.sciaf.2020.e00536