On a Projection Estimator of the Regression Function Derivative

Under mild assumptions on the estimation mapping, we show that this approximation is a weakly differentiable function of the parameters and its weak gradient, coined the Stein

(4) This assumption controls the decay of the Fourier coefficients of g, and thus the smoothness of g. It is a standard hypothesis usually adopted in the field of

Our approach is close to the general model selection method pro- posed in Fourdrinier and Pergamenshchikov (2007) for discrete time models with spherically symmetric errors which

Keywords and phrases: Nonparametric regression, Least-squares estimators, Penalized criteria, Minimax rates, Besov spaces, Model selection, Adaptive estimation.. ∗ The author wishes

In this section, we attempt to overcome irregularities (many critical points) or non-smoothness of the function H by making a convolution with some appropriate function, and we will

Keywords: Estimator selection; Model selection; Variable selection; Linear estimator; Kernel estimator; Ridge regression; Lasso; Elastic net;.. Random forest;

Solution: group the models ⇒ one estimator per dimension (e.g., empirical risk minimizer).. works for change-point

We derive a lower bound for the measure of performance of an empirical Bayes estimator for the scale parameter θ of a Pareto distribution by using a weighted squared-error loss