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Deconvolution Estimation in Measurement Error Models: The R Package decon

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  • Wang, Xiao-Feng
  • Wang, Bin

Abstract

Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples.

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  • Wang, Xiao-Feng & Wang, Bin, 2011. "Deconvolution Estimation in Measurement Error Models: The R Package decon," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i10).
    • Handle: RePEc:jss:jstsof:v:039:i10
    DOI: http://hdl.handle.net/10.18637/jss.v039.i10
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    Cited by:

    1. Hongwen Guo & Sandip Sinharay, 2011. "Nonparametric Item Response Curve Estimation With Correction for Measurement Error," Journal of Educational and Behavioral Statistics, , vol. 36(6), pages 755-778, December.
    2. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.
    3. Denys Pommeret, 2013. "A two-sample test when data are contaminated," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 501-516, November.
    4. Bin Wang & Shu-Guang Zhang & Xiao-Feng Wang & Ming Tan & Yaguang Xi, 2012. "Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-12, May.
    5. Abhra Sarkar & Bani K. Mallick & Raymond J. Carroll, 2014. "Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors," Biometrics, The International Biometric Society, vol. 70(4), pages 823-834, December.
    6. Wang, Xiao-Feng & Ye, Deping, 2015. "Conditional density estimation in measurement error problems," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 38-50.

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