Using SIMEX for smoothing-parameter choice in errors-in-variables problems
Using SIMEX for smoothing-parameter choice in errors-in-variables problems
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DOI：
10.1198/016214507000001355
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发表时间：
2008-03-01
影响因子：
3.7
通讯作者：
Hall, Peter
中科院分区：
文献类型：
--
作者：
Delaigle, Aurore;Hall, Peter
SIMEX方法是解决变量误差回归中曲线估计问题的有吸引力的方法，使用参数或半参数技术。然而，非参数方法通常是完全不同的类型，例如，基于内核，局部线性建模，脊，正交系列或样条。所有这些技术都涉及经验平滑参数选择的挑战性（并且没有得到很好的研究）问题。我们表明，SIMEX可以有效地用于选择平滑参数时，应用非参数方法的变量误差回归。特别是，我们提出了一种基于多个错误膨胀（或重新测量）的数据集和外推的方法。
SIMEX methods are attractive for solving curve estimation problems in errors-in-variables regression, using parametric or semiparametric techniques. However, nonparametric approaches are generally of quite a different type, being based on, for example, kernels, local-linear modeling, ridging, orthogonal series, or splines. All of these techniques involve the challenging (and not well studied) issue of empirical smoothing parameter choice. We show that SIMEX can be used effectively for selecting smoothing parameters when applying nonparametric methods to errors-in-variable regression. In particular, we suggest an approach based on multiple error-inflated (or remeasured) data sets and extrapolation.