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 ...
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 ...
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, ...
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 ...
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 ...
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.
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 ...
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