A note on penalized minimum distance estimation in nonparametric regression

The authors introduce a penalized minimum distance regression estimator. They show the estimator to balance, among a sequence of nested models of increasing complexity, the L¹ ‐approximation error of each model class and a penalty term which reflects the richness of each model and serves as a upper bound for the estimation error.