  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
