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 ...

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 ...

This project provides a fast Kernel Density Estimation (KDE) library implemented in Rust and Python scripts for benchmarking and comparison. It specifically focuses on an efficient Gaussian KDE method ...

Purpose: This study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how ...

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 ...

Under a single-index regression assumption, we introduce a new semiparametric procedure to estimate a conditional density of a censored response. The regression model can be seen as a generalization ...

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 ...

Abstract: The goal of regression analysis is to estimate the conditional mean of an input-output relation. But if the data has multimodality, highly asymmetric distribution or heteroscedastic noise, ...

In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive ...