Nonparametric Kernel Methods with Errors-in-Variables: Constructing Estimators, Computing them, and Avoiding Common Mistakes

DOI10.1111/anzs.12066zbMath1334.62006OpenAlexW2036434104MaRDI QIDQ2802866

Aurore Delaigle

Publication date: 27 April 2016

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/anzs.12066

zbMATH Keywords

bandwidthdeconvolutiondensity estimationregression estimationmeasurement errorsnonparametric curve estimationBerkson errorsMatlab code for deconvolution estimatorMatlab code for nonparametric regression with errors-in variablesR package \texttt{decon}

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Software, source code, etc. for problems pertaining to statistics (62-04)

Related Items (7)

Nonparametric estimation of cumulative distribution function from noisy data in the presence of Berkson and classical errors ⋮ A deconvolution path for mixtures ⋮ Estimation of survival and capture probabilities in open population capture-recapture models when covariates are subject to measurement error ⋮ Density estimation for circular data observed with errors ⋮ An alternative local polynomial estimator for the error-in-variables problem ⋮ Can we trust the bootstrap in high-dimension? ⋮ Nonparametric regression on Lie groups with measurement errors

Uses Software

• Matlab
• R
• BRugs
• decon

Cites Work

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