The researchers developed new robust ridge estimators to improve parameter estimates in linear regression models with outliers. These estimators are less affected by outliers compared to traditional methods. By using Monte Carlo simulations and real data, they found that the proposed robust ridge estimators outperformed other methods like least squares and M-estimation in terms of accuracy. Different ridge parameters were found to be more effective in different situations, showing the importance of selecting the right parameter for better results.