Ridge estimator is an alternative to ordinary least square estimator when
there is multicollinearity problem. There are many proposed estimators in
literature. In this paper, we propose new estimators which are modifications of
the estimator suggested by Lawless and Wang (1976). A Monte Carlo experiment
has been conducted for the comparison of the performances of the estimators.
Mean squared error (MSE) is used as a performance criterion. The benefits of
new estimators are illustrated using two real datasets. According to both
simulation results and applications, our new estimators have better
performances in the sense of MSE in most of the situations.