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 ...

標本$${D=\{x_1, \cdots, x_n\}}$$が従う確率密度$${f({\bm x})}$$をカーネル関数$${K({\bm x},{\bm x}')}$$を用いて、 $${\hat f_{KDE}({\bm x ...

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

We introduce a consistent estimator for the homology (an algebraic structure representing connected components and cycles) of level sets of both density and regression functions. Our method is based ...

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: 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 ...