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Filtered Kernel Density Estimation.
GEORGE MASON UNIV FAIRFAX VA CENTER FOR COMPUTATIONAL STATISTICS
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A modification of the kernel estimator for density estimation is proposed which allows the incorporation of local information about the smoothness of the density. The estimator used a small set of local bandwidths rather than a single global one as in the standard kernel estimator. It uses a set of filtering functions which determines the extent of influence of the local bandwidths. Various versions of the idea are discussed. The estimator is shown to be consistent and is illustrated by comparison to the single bandwidth kernel estimator for the case in which the filter functions are derived from finite mixture models.
APPROVED FOR PUBLIC RELEASE