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.

References in zbMATH (referenced in 20 articles , 1 standard article )

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  1. Guan, Zhong: Fast nonparametric maximum likelihood density deconvolution using Bernstein polynomials (2021)
  2. Linda Nab, Maarten van Smeden, Ruth H. Keogh, Rolf H.H. Groenwold: mecor: An R package for measurement error correction in linear regression models with a continuous outcome (2021) arXiv
  3. Slaoui, Yousri: Data-driven deconvolution recursive kernel density estimators defined by stochastic approximation method (2021)
  4. Al-Sharadqah, Ali; Mojirsheibani, Majid; Pouliot, William: On the performance of weighted bootstrapped kernel deconvolution density estimators (2020)
  5. Dabo-Niang, Sophie; Thiam, Baba: Kernel regression estimation with errors-in-variables for random fields (2020)
  6. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
  7. Yan, Ting; Qu, Liangqiang; Li, Zhaohai; Yuan, Ao: Conditional kernel density estimation for some incomplete data models (2018)
  8. Boutahar, Mohamed; Pommeret, Denys: A test for the equality of monotone transformations of two random variables (2016)
  9. Huang, Xianzheng: Dual model misspecification in generalized linear models with error in variables (2016)
  10. Pommeret, Denys: Comparing two mixing densities in nonparametric mixture models (2016)
  11. Lütkenöner, B.: A family of kernels and their associated deconvolving kernels for normally distributed measurement errors (2015)
  12. Wang, Xiao-Feng; Ye, Deping: Conditional density estimation in measurement error problems (2015)
  13. Auray, Stéphane; Eyquem, Aurélien; Jouneau-Sion, Frédéric: Modeling tails of aggregate economic processes in a stochastic growth model (2014)
  14. Delaigle, Aurore: Nonparametric kernel methods with errors-in-variables: constructing estimators, computing them, and avoiding common mistakes (2014)
  15. Sarkar, Abhra; Mallick, Bani K.; Carroll, Raymond J.: Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors (2014)
  16. Pommeret, Denys: A two-sample test when data are contaminated (2013)
  17. Wang, B.; Wertelecki, W.: Density estimation for data with rounding errors (2013)
  18. Sun, Wenguang; McLain, Alexander C.: Multiple testing of composite null hypotheses in heteroscedastic models (2012)
  19. Wang, Xiao-Feng; Ye, Deping: The effects of error magnitude and bandwidth selection for deconvolution with unknown error distribution (2012)
  20. Xiao-Feng Wang; Bin Wang: Deconvolution Estimation in Measurement Error Models: The R Package decon (2011) not zbMATH