An iterative bandwidth selector for kernel estimation of densities and their derivatives

A bandwidth selection rule which proved to be useful and effective for nonparametric kernel regression is modified to be suitable for estimation of a density and its derivatives. Various versions of the rule are considered. Theoretical properties are derived. A simulation study compares its finite-sample behavior with that of other bandwidth selectors.