Methodology for Non-Parametric Deconvolution When the Error Distribution is Unknown

DOI10.1111/rssb.12109zbMath1411.62092OpenAlexW2161676148MaRDI QIDQ5743233

Hall, Peter, Aurore Delaigle

Publication date: 9 May 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/rssb.12109

zbMATH Keywords

kernel smoothingerrors in variablesmeasurement errorsnonparametric density estimation

Mathematics Subject Classification ID

Density estimation (62G07)

Related Items (11)

Semiparametric Estimation of the Distribution of Episodically Consumed Foods Measured With Error ⋮ NONPARAMETRIC SIGNIFICANCE TESTING IN MEASUREMENT ERROR MODELS ⋮ Estimation of varying coefficient models with measurement error ⋮ Sparse covariance matrix estimation in high-dimensional deconvolution ⋮ Dynamic deconvolution and identification of independent autoregressive sources ⋮ Density deconvolution for generalized skew-symmetric distributions ⋮ Estimation of the Boundary of a Variable Observed With Symmetric Error ⋮ Unnamed Item ⋮ Uniform confidence bands for nonparametric errors-in-variables regression ⋮ Uniform confidence bands in deconvolution with unknown error distribution ⋮ Parametric estimation of hidden Markov models by least squares type estimation and deconvolution

This page was built for publication: Methodology for Non-Parametric Deconvolution When the Error Distribution is Unknown