$\beta$-Divergence loss for the kernel density estimation with bias reduced

Abstract

Allthough nonparametric kernel density estimation with bias reduce is nowadays a standard technique in explorative data-analysis, there is still a big dispute on how to assess the quality of the estimate and which choice of bandwidth is optimal. This article examines the most important bandwidth selection methods for kernel density estimation with bias reduce, in particular, normal reference, least squares cross-validation, biased crossvalidation and $\beta$-Divergence loss. Methods are described and expressions are presented. We will compare these various bandwidth selector on simulated data. As an example of real data, we will use econometric data sets CO2 per capita in example 1 and the second