Data-driven deconvolution recursive kernel density estimators defined by stochastic approximation method

DOI10.1007/s13171-019-00182-3zbMath1465.62073OpenAlexW2992218927WikidataQ126621095 ScholiaQ126621095MaRDI QIDQ2023841

Yousri Slaoui

Publication date: 3 May 2021

Published in: Sankhyā. Series A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13171-019-00182-3

zbMATH Keywords

smoothingcurve fittingdeconvolutionbandwidth selectiondensity estimationstochastic approximation algorithm

Mathematics Subject Classification ID

Density estimation (62G07) Numerical smoothing, curve fitting (65D10) Stochastic approximation (62L20)

Related Items (1)

The stochastic approximation method for recursive kernel estimation of the conditional extreme value index

Uses Software

• decon

Cites Work

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• Strong consistency and rates for deconvolution of multivariate densities of stationary processes
• Local bandwidth selectors for deconvolution kernel density estimation
• On the optimal rates of convergence for nonparametric deconvolution problems
• Practical bandwidth selection in deconvolution kernel density estimation
• The stochastic approximation method for the estimation of a multivariate probability density
• Estimation of integrated squared density derivatives
• Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes
• Mean square error properties of density estimates
• Finite sample performance of deconvolving density estimators
• Optimal bandwidth selection for semi-recursive kernel regression estimators
• Bootstrap bandwidth selection in kernel density estimation from a contaminated sample
• Bandwidth selection for kernel distribution function estimation
• Bandwidth selection for recursive kernel density estimators defined by stochastic approximation method
• 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
• Deconvolving kernel density estimators
• Revisiting R\'ev\'esz's stochastic approximation method for the estimation of a regression function
• Density Estimation in the Presence of Heteroscedastic Measurement Error
• Optimal Rates of Convergence for Deconvolving a Density
• Deconvolution with supersmooth distributions
• Data-driven deconvolution
• Estimation of Integrated Squared Density Derivatives from a Contaminated Sample
• On the effect of misspecifying the error density in a deconvolution problem
• Asymptotic normality of nonparametric kernel type deconvolution density estimators: crossing the Cauchy boundary
• Large and moderate deviation principles for averaged stochastic approximation method for the estimation of a regression function
• Large and moderate deviation principles for recursive kernel density estimators defined by stochastic approximation method
• Asymptotic Normality of Kernel-Type Deconvolution Estimators
• Measurement Error in Nonlinear Models
• Regularly Varying Sequences
• Boundary bias correction for nonparametric deconvolution
• Plug‐in bandwidth selector for recursive kernel regression estimators defined by stochastic approximation method

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