Comparison of Data - Driven Bandwidth Selectors
نویسنده
چکیده
This paper provides a comparison. on three levels. 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-val idation. partitioned cross-val idation. and a plug-in rule. The levels of comparison are: asymptotic rate of convergence to the optimum, explici t calculation in the case of one target density. 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.
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