Asymptotic normality of nonparametric kernel type deconvolution density estimators: crossing the Cauchy boundary

DOI10.1080/10485250310001644574zbMath1216.62074arXivmath/0212007OpenAlexW2088109857MaRDI QIDQ4819564

Hae-Won Uh, A. J. van Es

Publication date: 27 September 2004

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0212007

zbMATH Keywords

asymptotic normalitykernel estimationdeconvolution

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20)

Related Items (14)

Density testing in a contaminated sample ⋮ Estimation of varying coefficient models with measurement error ⋮ Deconvolution for an atomic distribution ⋮ On the performance of weighted bootstrapped kernel deconvolution density estimators ⋮ Confidence sets in nonparametric calibration of exponential Lévy models ⋮ Weak convergence of the supremum distance for supersmooth kernel deconvolution ⋮ Nonparametric density estimation from data with a mixture of Berkson and classical errors ⋮ Integrated Square Error Asymptotics for Supersmooth Deconvolution ⋮ Data-driven deconvolution recursive kernel density estimators defined by stochastic approximation method ⋮ Asymptotic normality of the deconvolution kernel density estimator under the vanishing error variance ⋮ Goodness-of-fit testing of error distribution in linear measurement error models ⋮ A review of uncertainty quantification for density estimation ⋮ Asymptotic Normality of Kernel-Type Deconvolution Estimators ⋮ Consistency and asymptotic normality for a nonparametric prediction under measurement errors

Uses Software

• KernSmooth

Cites Work

• Unnamed Item
• On the optimal rates of convergence for nonparametric deconvolution problems
• Simple kernel estimators for certain nonparametric deconvolution problems
• Fourier methods for estimating mixing densities and distributions
• Deconvolving kernel density estimators
• Deconvolution with arbitrarily smooth kernels

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