Robust regression trees based on M-estimators

The paper addresses the problem of robustness of regression trees with respect to unusual data. New robust tree-based procedures are proposed which are obtained by introducing in the tree building phase some objective functions based on M-estimation methodology and already used in the linear robust regression approach. The performances of the new procedures are evaluated through Monte Carlo experiments, in which regression trees based on the least squares and the least absolute deviation criteria are also examined.