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.
authors: Department of Quantitative Health Science/Biostatistics Section, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland OH 44195, United States of America.
authors: Xiao-Feng
authors: XF
authors: Wang
authors: Bin
authors: B
authors: Wang
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