Ridge regression, which defines a class of estimators indexed by a biasing parameter k, is an alternative to the ordinary least squares (OLS) estimator in the multiple linear regression model. In this paper, the problem of choosing the biasing ridge regression parameter k is considered. Two methods of specifying k are proposed here and evaluated in terms of mean square error (MSE). Comparisons are made with Hoerl and Kennard [4] and Hoerl et al. [5] proposed choice of k. The results of the simulation study indicate that, with respect to MSE criteria, the OLS estimator is dominated by these estimators in all investigated cases.

linear regression model, multicollinearity, ridge regression estimators, simulation study.