Rate of convergence of nonparametric estimates of maximum-likelihood type
The authors obtain the rate of convergence for non-parametric regression M-estimators in \(L_ 2\). For a class of smooth, monotonic and convex functions, they show that this rate cannot be improved (to within a constant). A series of special cases is considered. The rate of convergence of the non-linear M-estimators is better than that of any linear estimator.
scientific article; zbMATH DE number 3998995