  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.
