The Comparison Between Different Approaches to Overcome the Multicollinearity Problem in Linear Regression Models

Main Article Content

Hazim Mansoor Gorgees Fatimah Assim Mahdi

Abstract

    In the presence of multi-collinearity problem, the parameter estimation method based on the ordinary least squares procedure is unsatisfactory. In 1970, Hoerl and Kennard insert analternative method labeled as estimator of ridge regression.


In such estimator, ridge parameter plays an important role in estimation. Various methods were proposed by many statisticians to select the biasing constant (ridge parameter). Another popular method that is used to deal with the multi-collinearity problem is the principal component method. In this paper,we employ the simulation technique to compare the performance of principal component estimator with some types of ordinary ridge regression estimators based on the value of the biasing constant (ridge parameter). The mean square error (MSE) is used as a criterion to assess the performance of such estimators.

Article Details

How to Cite
GORGEES, Hazim Mansoor; MAHDI, Fatimah Assim. The Comparison Between Different Approaches to Overcome the Multicollinearity Problem in Linear Regression Models. Ibn AL- Haitham Journal For Pure and Applied Science, [S.l.], v. 31, n. 1, p. 212-221, may 2018. ISSN 2521-3407. Available at: <http://jihcoed.com/ihj/index.php/j/article/view/1841>. Date accessed: 14 dec. 2018. doi: http://dx.doi.org/10.30526/31.1.1841.
Section
mathematics