Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those ...
Kernel density estimation (KDE) is a versatile nonparametric approach to infer continuous probability distributions from finite samples. By superimposing smooth kernel functions—most commonly Gaussian ...
Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...
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 ...
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