Local linear kernel estimation of the discontinuous regression function

We address the problems of estimating the discontinuous regression function and also its jump points. We propose a method in two steps: we first estimate the jumps and finally the regression function is estimated by an adapted version of a local linear smoother which makes use of the estimated jumps. The practical performance of the proposed method is evaluated by using simulation studies and an application to a real-life problem.