Estimation of the Derivatives of a Function in a Convolution Regression Model with Random Design

Abstract

A convolution regression model with random design is considered. We investigate the estimation of the derivatives of an unknown function, element of the convolution product. We introduce new estimators based on wavelet methods and provide theoretical guarantees on their good performances.