Comparison of Data-Driven Bandwidth Selectors
- 1 March 1990
- journal article
- research article
- Published by JSTOR in Journal of the American Statistical Association
- Vol. 85 (409) , 66-72
- https://doi.org/10.2307/2289526
Abstract
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.Keywords
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This publication has 2 references indexed in Scilit:
- Automatic smoothing parameter selection: A surveyEmpirical Economics, 1988
- Estimation of integrated squared density derivativesStatistics & Probability Letters, 1987