The aim of this thesis is to provide two extensions to the theory of nonparametric
kernel density estimation that increase the scope of the technique.
The basic ideas of kernel density estimation are not new, having been proposed by
Rosenblatt  and extended by Parzen . The objective is that for a given set of
data, estimates of functions of the distribution of the data such as probability densities
are derived without recourse to rigid parametric assumptions and allow the...[Show more]
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