On sufficient conditions for a Gaussian approximation of kernel estimates for distribution densities

DOI10.33048/SEMI.2018.15.127zbMath1417.62064OpenAlexW3015663888MaRDI QIDQ2633391

Andrey Sergeevich Kartashov, Aleksandr Ivanovich Sakhanenko

Publication date: 8 May 2019

Published in: Sibirskie Èlektronnye Matematicheskie Izvestiya (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.33048/semi.2018.15.127

zbMATH Keywords

Brownian motionfunction of bounded variationkernel density estimators

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Brownian motion (60J65)

Cites Work

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• The oscillation behavior of empirical processes
• Kernel density estimators: convergence in distribution for weighted sup-norms
• Weighted uniform consistency of kernel density estimators.
• About conditions of Gaussian approximation of kernel estimates for distribution density
• Asymptotics of Suprema of Weighted Gaussian Fields with Applications to Kernel Density Estimators
• Probability Inequalities for Sums of Bounded Random Variables
• Regularly varying functions

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