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

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 ...

Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

The Annals of Statistics, Vol. 23, No. 1 (Feb., 1995), pp. 1-10 (10 pages) Variable window width kernel density estimators, with the width varying proportionally to the square root of the density, ...

We discuss and analyze some recent literature that introduced pioneering methods in econophysics. In doing so, we review recent methods of estimating the volatility, volatility of volatility, and ...

This is a preview. Log in through your library . Abstract Sequential Monte Cario (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a ...

Abstract: This paper examines parametric density estimation using a variable weighted sum of Gaussian kernels, where the weights may take positive and negative values. Various statistical properties ...