The comparison between different approaches to overcome the multicollinearity problem in linear regression models

Abstract EN

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 an alternative 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.