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 ...

Estimation of the value of a density function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique are presented. The generalized jackknife method is ...

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 ...

Several papers have recommended the Champernowne distribution to describe operational risk losses. This paper compares the tail performance of the Champernowne transformed kernel density estimator, ...

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, ...