Nonparametric simulation extrapolation for measurement-error models

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.