Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...

Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

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Abstract: Kernel density estimation (KDE), a flexible nonparametric technique unconstrained by specific data distribution assumptions, is extensively employed in fault modeling. However, its ...

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 ...

This project implements and compares nonparametric estimators for convolution densities ψ = f ⋆ g, where f and g are unknown probability density functions. The implementation uses higher-order kernels ...

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