An Asymptotically Efficient Solution to the Bandwidth Problem of Kernel Density Estimation. Revision.
NORTH CAROLINA UNIV AT CHAPEL HILL DEPT OF STATISTICS
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A data-driven method of choosing the bandwidth, h, of a kernel density estimator is proposed. It is seen that this means of selecting h is asymptotically equivalent to taken the h that minimizes a certain weighted version of the mean integrated square error. Thus, for a given kernel function, the bandwidth can be chosen optimally without making precise smoothness assumptions on the underlying density. The proposed technique is a modification of cross-validation.
- Numerical Mathematics