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, ...

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 ...

Recall that kernel density estimation of a point pattern involves placing a kernel function at each point and averaging over all points to estimate density. Two things are primarily under our control ...

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 ...