decon: Deconvolution Estimation in Measurement Error Models. This package contains 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.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Boutahar, Mohamed; Pommeret, Denys: A test for the equality of monotone transformations of two random variables (2016)
- Pommeret, Denys: Comparing two mixing densities in nonparametric mixture models (2016)
- Wang, Xiao-Feng; Ye, Deping: Conditional density estimation in measurement error problems (2015)
- Delaigle, Aurore: Nonparametric kernel methods with errors-in-variables: constructing estimators, computing them, and avoiding common mistakes (2014)
- Pommeret, Denys: A two-sample test when data are contaminated (2013)
- Sun, Wenguang; McLain, Alexander C.: Multiple testing of composite null hypotheses in heteroscedastic models (2012)
- Wang, Xiao-Feng; Ye, Deping: The effects of error magnitude and bandwidth selection for deconvolution with unknown error distribution (2012)