The presence of measurement error is a widespread issue, which, when ignored, can render the results of an analysis unreliable. Numerous corrections for the effects of measurement error have been proposed and studied, often under the assumption of a normally distributed, additive measurement-error model. In many situations, observed data are nonsymmetric, heavy-tailed, or otherwise highly non-normal. In these settings, correction techniques relying on the assumption of normality are undesirable. We propose an extension of simulation extrapolation that is nonparametric in the sense that no specific distributional assumptions are required on the error terms. The technique can be implemented when either validation data or replicate measurements are available, and is designed to be immediately accessible to those familiar with simulation extrapolation.

中文翻译：

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测量误差模型的非参数模拟外推

测量误差的存在是一个普遍存在的问题，如果忽视它，可能会导致分析结果不可靠。人们经常在正态分布、加性测量误差模型的假设下提出并研究了许多针对测量误差影响的修正。在许多情况下，观察到的数据是不对称的、重尾的或高度非正态的。在这些情况下，依赖于正态性假设的校正技术是不可取的。我们提出了非参数模拟外推的扩展，即误差项不需要特定的分布假设。当验证数据或重复测量可用时，可以实施该技术，并且设计为熟悉模拟外推的人员可以立即使用。