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 correction is the simulation extrapolation method, which provides a flexible way of correcting for the effects of error in a wide variety of models, when the errors are approximately normally distributed. However, 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 it shares the general structure of the standard simulation extrapolation procedure, making it immediately accessible for those familiar with this technique.

翻译：测量误差的普遍存在是一个广泛的问题，若忽视它可能导致分析结果不可靠。针对测量误差的影响，已有多种修正方法被提出和研究，这些方法通常假设误差服从正态可加测量误差模型。其中一种修正方法是模拟外推法，当误差近似服从正态分布时，该方法能以灵活方式纠正多种模型中的误差影响。然而在许多实际场景中，观测数据往往呈现非对称性、重尾性或高度非正态特征。在此类情况下，依赖正态性假设的修正技术并不理想。我们提出模拟外推法的扩展版本，该方法在非参数意义上无需对误差项作具体分布假设。该技术可在获得验证数据或重复测量数据时实施，且保留了标准模拟外推法的一般结构，便于熟悉该技术的研究者直接应用。