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 ...

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 ...

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 ...

Abstract: Analog cooperative beamforming (ACB) improves physical-layer security (PLS) by enabling phase-coordinated transmissions among spatially distributed nodes without exchanging channel state ...

Abstract: In various applications, the importance of localization has increased significantly. In this study, we propose a method combining a filter bank and kernel density estimation (KDE) for robust ...

gaussian_kde provides multivariate kernel density estimation (KDE) with Gaussian kernels and optionally weighed data points. Given a dataset $X = {x_1, \cdots, x_n ...

A kernel density curve may follow the shape of the distribution more closely. To construct a normal kernel density curve, one parameter is required: the bandwidth .The value of determines the degree ...