The performance of some new estimated ridge parameter regression model

In the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produces large variations in the sample. To overcome this problem, several estimators have been recommended. One of these estimators that has been proposed to reduce the impact of the multicollinearity problem was the estimator of the ridge regression, which leads to biased estimated coefficients, but it has a smaller variance than ordinary least squares estimators, which may have a smaller mean square error (MSE). In this paper, we review 50 kinds of ridge parameter (m) that a use for estimating the ordinary ridge regression estimator (ORR) in addition to that, we propose 10 of new types of m based on different approaches. Both of simulation study and numerical example are used to compare these estimators of m using estimated of mean squared error (EMSE) to study the performance of them. The Simulation and on example results show that, the new proposed estimators of m can be used instead of audit others.