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. One such method is simulation extrapolation, or SIMEX. In many situations observed data are non-symmetric, heavy-tailed, or otherwise highly non-normal. In these settings, correction techniques relying on the assumption of normality are undesirable. We propose an extension to the simulation extrapolation method which is nonparametric in the sense that no specific distributional assumptions are required on the error terms. The technique is implemented when either validation data or replicate measurements are available, and is designed to be immediately accessible for those familiar with simulation extrapolation.

翻译：测量误差是一个普遍存在的问题，若忽略此问题，可能导致分析结果不可靠。针对测量误差的影响，已有多种校正方法被提出并研究，这些方法通常假设误差服从正态分布且具有可加性。模拟外推法（SIMEX）便是其中之一。然而在许多实际场景中，观测数据呈现非对称、厚尾或高度非正态的特征。在此类情况下，依赖正态性假设的校正技术并不理想。本文提出了一种模拟外推法的扩展方法，该方法在非参数意义下无需对误差项设定具体分布假设。当存在验证数据或重复测量数据时，该技术即可实施，并且特别设计为便于熟悉模拟外推法的研究者直接应用。