The classical least squares procedures can produce estimate having a large mean square error (MSE) when the explanatory variables are highly correlated. The estimation of the regression parameters for the linear regression model with ill-conditioned explanatory variables are discussed in this paper. We propose some improved estimators, namely, the unrestricted ridge regression estimator, restricted ridge regression estimator, preliminary test ridge regression estimator, shrinkage ridge regression estimator, and positive rule ridge regression estimators in this talk. The performances of the proposed estimators are compared based on the quadratic bias and risk function under both null and alternative hypotheses, which specify certain restric tions on the regression parameters. The conditions of superiority of the proposed estimators for departure and ridge parameters are given. It is demonstrated that unlike the positive rule shrinkage (PR) estimator which dominates both unrestricted and shrinkage estimators, the positive rule ridge regression estimators (PRRRE) utilizes both sample and non-sample infor mation but does not outperform the unrestricted and shrinkage ridge regression estimators for an ill-conditioned data. As an application a real life example will be discussed.