New Approach for Bandwidth Selection in the Kernel Density Estimation Based On

Abstract

The choice of bandwidth is crucial to the kernel density estimation (KDE).Various bandwidth selection methods for KDE, least squares cross-validation (LSCV) and Kullback-Leibler cross-validation are proposed. We propose a method to select the optimal bandwidth for the KDE. The idea behind this method is to generalize the LSCV method, using the measure of ; divergence -β HAMZA DHAKER et al. 58 and to see the improvement in our method, we will compare these ( )bandwidth selector with a normal reference (NR), the last squares crossvalidation (LSCV), the Sheather and Jones (SJ) method, and the generalized ( )bandwidth selector, on simulated data. The use of the various practical bandwidth selectors is illustrated on a real data example.

Comparison of kernel density function in detecting effects of daily emission of Sulphur (IV) oxide from an industrial plant

“…The tuning parameter also called smoothing parameter or bandwidth that determines the degree of smoothness of the kernel estimate is very crucial in detecting the behavior of data (Wand and Jones, 1995;Chacón, 2009;Chacón and Duong, 2010;Zhang, 2015;Borrajo et al, 2017;Dhaker et al, 2018). Different techniques of obtaining the tuning parameter have been propounded by several authors and no single approach is universally accepted in all conditions, hence new tuning parameter selectors are being proposed.…”

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

“…To explore the most relevant bandwidth selection methods in density estimation for complete data see the reviews of Turlach [19], Cao et al [3], Jones et al [8] or Heidenreich et al (2013), Mammen et al ([11] and [12]), and the recent work on β-Divergence for Bandwidth Selection by Dhaker and al. [5]. Our aim in this paper is to propose and compare several bandwidth selection procedures for the kernel density estimators introduced by Xie and Wu [21].…”