Optimization in Non-Parametric Regression

Non parametric regression is approached through linear estimation, a less restrictive view than the kernel approach since the solution can be adaptative for any pattern of distribution of the abscissae. Local polynomial regression happens to be optimal in the sense of minimum variance for a given order of biais reduction. This last notion is in fact the main issue. Links with kernel theory and computational aspects are given.