Bandwidth Selection for Kernel Regression: a Survey

This paper is concerned with nonparametric estimation of a regression function. The behaviour of kernel estimates depends on a smoothing parameter (i.e. the bandwidth). Bandwidth choice turns out to be of particular importance as well for practical use as to insure good asymptotic properties of the estimate. Various techniques have been proposed in the past ten last years to select optimal values of this parameter. This paper presents a survey on theoretical results concerned with bandwidth selection.