Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Xiao-Feng Wang, Bin Wang
Title: Deconvolution Estimation in Measurement Error Models: The R Package decon
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.

Page views:: 9080. Submitted: 2010-03-21. Published: 2011-03-09.
Paper: Deconvolution Estimation in Measurement Error Models: The R Package decon     Download PDF (Downloads: 10939)
decon_1.2-2.tar.gz: R source package Download (Downloads: 734; 53KB)
v39i10.R: R example code from the paper Download (Downloads: 835; 5KB)

DOI: 10.18637/jss.v039.i10

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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.