Researchers developed new robust ridge M-estimators to improve the accuracy of linear regression models when dealing with outliers in the data. These new estimators outperformed existing methods in reducing errors and handling multicollinearity issues. The study showed that the new estimators are more efficient in estimating robust ridge parameters, especially in situations with high levels of multicollinearity and outliers in the y-variable.