Moderate deviation principles for nonparametric recursive distribution estimators using Bernstein polynomials

DOI10.1007/S13163-021-00384-0zbMath1493.62190OpenAlexW3118457805MaRDI QIDQ2072236

Yousri Slaoui

Publication date: 26 January 2022

Published in: Revista Matemática Complutense (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13163-021-00384-0

zbMATH Keywords

large and moderate deviations principlesBernstein polynomialstochastic approximation algorithmdistribution estimation

Mathematics Subject Classification ID

Density estimation (62G07) Stochastic approximation (62L20) Large deviations (60F10)

Cites Work

• Unnamed Item
• The stochastic approximation method for the estimation of a multivariate probability density
• Large deviations for past-dependent recursions.
• The method of stochastic exponentials for large deviations
• Application of Bernstein polynomials for smooth estimation of a distribution and density function
• On estimating distribution functions using Bernstein polynomials
• Recursive density estimators based on Robbins-Monro's scheme and using Bernstein polynomials
• The stochastic approximation method for estimation of a distribution function
• A companion for the Kiefer-Wolfowitz-Blum stochastic approximation algorithm
• A unified theory of regularly varying sequences
• Efficient and robust density estimation using Bernstein type polynomials
• Recursive distribution estimator defined by stochastic approximation method using Bernstein polynomials
• Bernstein polynomial model for nonparametric multivariate density
• Large and moderate deviation principles for nonparametric recursive kernel distribution estimators defined by stochastic approximation method
• Regularly Varying Sequences
• Large deviations

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