SFB 303 Discussion Paper No. A-414

Author: Engel, Joachim, Eva Herrmann, and Theo Gasser
Title: An Iterative Bandwidth Selector for Kernal Estimation of Densities and their Derivatives
Abstract: A bandwidth selection rule which proved to be useful and effective for nonparametric kernal 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.
Keywords: Bandwidth Selection, Density estimation, Density derivatives, Kernel estimators, Plug-in method, Smoothing
Creation-Date: July 1993
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