The use of robust regression methods is common in practical situations due to the presence of outliers. This work proposes a robust regression method that re-weighted the outliers observations considering type-exponential kernel functions. The convergence of the parameter estimate algorithm is guaranteed with a low computational cost. A
comparative study between the proposed regression method (ETKRR) against some classical robust approaches and the OLS method is considered. We have considered synthetic datasets with X-axis outliers, Y-axis outliers and leverage points, in a Monte Carlo simulation framework with different sample sizes and percentage of outliers. The
results have demonstrated that the ETKRR approach presented a competitive (or best) performance in simulation scenarios that are similar to those found in real problems. Applications to real datasets has showed the usefulness of the proposed method.
