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Authors: Xiao-Feng Wang, Bin Wang
Title: [download]
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Deconvolution Estimation in Measurement Error Models: The R Package decon
Reference: Vol. 39, Issue 10, Mar 2011
Submitted 2010-03-21, Accepted 2010-11-04
Type: Article
Abstract:

Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples.

Paper: [download]
(6426)
Deconvolution Estimation in Measurement Error Models: The R Package decon
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Supplements: [download]
(563)
decon_1.2-2.tar.gz: R source package
(application/x-gzip, 53.7 KB)
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v39i10.R: R example code from the paper
(application/octet-stream, 5.8 KB)
Resources: BibTeX | OAI
Creative Commons License
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.
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