UDC 004.42
BANDWIDTH SELECTION ALGORITHM BASED ON KERNEL DENSITY ESTIMATION METHOD
T. D. Luu, post-graduate student, RSREU, Ryazan, Russia;
orcid.org/0000-0003-3347-1469, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Nonparametric density estimation serves as a key instrument in statistical analysis, however, traditional methods like kernel estimates relying on normal reference rule are hindered by excessive smoothing and lim ited adaptability. The aim of this work is to modernize bandwidth selection method based on plug-in ap proach which completely eliminates the use of normal reference rules that negatively affect the performance of plug-in methods. The obtained results demonstrate more accurate density estimation compared to normal reference rule as evidenced by a reduction in integrated squared error of up to 90 %. A modernized method provides improved density estimation accuracy for complex distributions without numerical optimization while maintaining computational efficiency.
Key words: : bandwidth, nonparametric density estimation, kernel density estimation.