2024, Vol. 5, Issue 1, Part B
A modified rule-of-thumb method for kernel density estimation
Author(s): Elsidieg I Belhaj
Abstract: Kernel density estimation (KDE) is a widely used nonparametric technique for estimating the probability density function (PDF) of a random variable. However, the performance of KDE depends largely on the choice of the bandwidth parameter, which controls the trade-off between bias and variance in the estimation. In this study, we investigate the Silverman method for selecting the bandwidth for univariate continuous PDFs, and propose a modified method that improves the accuracy and efficiency of the estimation. We use simulation to compare the two methods, and show that the modified method achieves lower mean squared error (MSE) and mean integrated squared error (MISE) than the Silverman method.
Pages: 143-149 | Views: 1009 | Downloads: 719
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How to cite this article:
Elsidieg I Belhaj. A modified rule-of-thumb method for kernel density estimation. Journal of Mathematical Problems, Equations and Statistics. 2024; 5(1): 143-149.
