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Comparison of Data-Driven Bandwidth Selectors
Byeong U. Park and J. S. Marron
Journal of the American Statistical Association
Vol. 85, No. 409 (Mar., 1990), pp. 66-72
Stable URL: http://www.jstor.org/stable/2289526
Page Count: 7
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This article compares 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 a 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 sufficiently 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.
Journal of the American Statistical Association © 1990 American Statistical Association