Non-Parametric Regression Estimation from Data Contaminated by a Mixture of Berkson and Classical Errors

DOI10.1111/j.1467-9868.2007.00614.xOpenAlexW2141828312WikidataQ33498511 ScholiaQ33498511MaRDI QIDQ5088200

Hall, Peter, Aurore Delaigle, Raymond J. Carroll

Publication date: 11 July 2022

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

Full work available at URL: https://europepmc.org/articles/pmc2733794

zbMATH Keywords

kernel methoddeconvolutionorthogonal seriesmeasurement errorerrors in variablessmoothing parameterBerkson errorsradiation dosimetry

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Mean field variational Bayesian inference for nonparametric regression with measurement error, Modeling body height in prehistory using a spatio-temporal Bayesian errors-in-variables model, Estimating multivariate density and its derivatives for mixed measurement error data, Strong consistency of wavelet estimators for errors-in-variables regression model, Optimal rates of convergence for nonparametric regression estimation under anisotropic Hölder condition, Kernel regression for errors-in-variables problems in the circular domain, Estimation of nonparametric regression models with a mixture of Berkson and classical errors, Rank tests and regression rank score tests in measurement error models, Nonparametric Berkson regression under normal measurement error and bounded design, Regression estimation under strong mixing data, Rate-optimal nonparametric estimation in classical and Berkson errors-in-variables problems, Identification of nonparametric monotonic regression models with continuous nonclassical measurement errors, The empirical process of residuals from an inverse regression, Simultaneous inference for Berkson errors-in-variables regression under fixed design