Deconvolution Estimation in Measurement Error Models
Description
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.