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The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...

We consider the Parzen-Rosenblatt kernel density estimate on Rd with data-dependent smoothing factor. Sufficient conditions on the asymptotic behavior of the smoothing factor are given under which the ...