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 ...

ABSTRACT: This paper presents a nonparametric method for computing the Value at Risk (VaR) based on efficient density estimators with Fejér-type kernel functions and empirical bandwidths obtained from ...

where K 0 (·) is a kernel function, is the bandwidth, n is the sample size, and x i is the i th observation. The KERNEL option provides three kernel functions (K 0): normal, quadratic, and triangular.

In this paper we show how one canimplement in practice the bandwidth selection in deconvolution recursive kernel estimators of a probability density function defined by the stochastic approximation ...

This is a preview. Log in through your library . Abstract A kernel density estimator is defined to be admissible if no other kernel estimator has (among all densities ...

In the kde_model class as well as in the ProbabilisticPyMC3Model class, points of maximum probability density are calculated by numerically minimizing a function. This method is only able to find ...

Abstract: The High-Precision Bipolar Disorder Detection through Dynamic Laterality and Kernel Density Estimation-based Neural Networks (HBDDKN) model offers a novel approach for classifying ...

Abstract: Particle filters (PFs) are widely used for state estimation in signal processing. However, the standard PFs suffer from weight degeneracy and sample impoverishment. To overcome these, we ...