Computational study introduces a multivariate kernel smoothing library in R, enabling automated bandwidth matrix selection for complex density estimation.
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.
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Tarn Duong (2007) studied this question.
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