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.


News

What's new?

The Fast Fourier Transform (FFT) algorithm has been implemented to the estimation in measurement error models. The plot and print functions have been updated.

The package provides a collection of function series of functions to deal with nonparametric measurement error problems using the deconvolution kernel methods. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the FFT algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several bandwidth selection functions are also developed in the package.

Reference manual

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install.packages("decon")

1.3-4 by Xiao-Feng Wang, a month ago


Browse source code at https://github.com/cran/decon


Authors: Xiao-Feng Wang , Bin Wang


Documentation:   PDF Manual  


GPL (>= 3) license



Imported by AsyK, lpme.

Depended on by UMR.


See at CRAN