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

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, ...

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 ...

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 ...

Abstract: In this work, a kernel-based Ensemble Gaussian Mixture Probability Hypothesis Density (EnGM-PHD) filter is presented for multi-target filtering applications. The EnGMPHD filter combines 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 ...

Abstract: In multitarget tracking (MTT), the prior information of background parameters, such as clutter intensity and detection probability, exerts a significant influence on the performance of the ...

We propose a method for reconstructing a probability density function (pdf) from a sample of an n-dimensional probability distribution. The method works by iteratively applying some simple ...