Deconvolution Estimation in Measurement Error Models: The R Package decon.
Deconvolution Estimation in Measurement Error Models: The R Package decon.
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DOI：
10.18637/jss.v039.i10
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发表时间：
2011-03
影响因子：
5.8
通讯作者：
Xiao-Feng Wang;Bin Wang-
中科院分区：
文献类型：
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作者：
Xiao-Feng Wang;Bin Wang-
来自许多科学领域的数据往往带有测量误差。污染数据的密度或分布函数估计和变量有误差的非参数回归是测量误差模型中的两个重要问题。在本文中，我们提出了一个新的软件包DECON，它包含了一组使用反卷积核方法来处理测量误差问题的函数。该函数允许误差为同方差或异方差。为了使反卷积估计器在R中的计算效率更高，我们将无误差数据密度估计的快速傅立叶变换算法应用于反卷积核估计。我们讨论了反褶积方法中平滑参数的实际选择，并通过仿真和实例说明了该程序包的使用。
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.