Deconvolution Estimation in Measurement Error Models // A
collection of functions to deal with nonparametric measurement
error problems using deconvolution kernel methods. We focus two
measurement error models in the package: (1) an additive
measurement error model, where the goal is to estimate the
density or distribution function from contaminated data; (2)
nonparametric regression model with errors-in-variables. The R
functions allow the measurement errors to be either
homoscedastic or heteroscedastic. To make the deconvolution
estimators computationally more efficient in R, we adapt the
"Fast Fourier Transform" (FFT) algorithm for density estimation
with error-free data to the deconvolution kernel estimation.
Several methods for the selection of the data-driven smoothing
parameter are also provided in the package. See details in:
Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
measurement error models: The R package decon. Journal of
Statistical Software, 39(10), 1-24.