SFB 303 Discussion Paper No. A - 215

Author: Park, Byeong, and J. S. Marron
Title: Comparison of Data-Driven Bandwidth Selectors
Abstract: This paper provides a comparison of several promising data-driven methods for selecting the bandwidth of a kernel density estimator. The methods compared are: least squares cross-validation, biased cross-validation and plug-in rule. The comparison is done by asymptotic rate of convergence to the optimum and a simulation study. It is seen that the plug-in bandwidth is usually most efficient when the underlying density is sufficient smooth, but is less robust when there is not enough smoothness present. We believe the plug-in rule is the best of those currently available, but there is still room for improvement.
Keywords: cross-validation, data driven bandwidth selection, density estimation, kernel estimators, plug-in method
Creation-Date: December 1988
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