Cook & Stefanski(1994, JASA) introduced a novel, computer-intensive estimation procedure for measurement error models, called SIMEX, for simulation and extrapolation. We refer to this method as parametric SIMEX because data with additional amounts of measurement error (pseudo data) are generated via simulation from a parametric family of distributions. We propose a different version of the SIMEX method, called empirical SIMEX, where the pseudo data are generated without the aid of a parametric model. The empirical SIMEX method automatically handles the inherent homogeneity or heterogeneity of the measurement error variances and therefore does not require any assumptions on the measurement error variance structure. We call the parametric and empirical SIMEX methods as remeasurement methods; they are similar in flavor to resampling methods such as the jackknife and bootstrap. In this talk, I'll give a brief introduction to measurement error models and describe the SIMEX algorithm. I'll then outline a few important results and illustrate the application of these methods to data from the Framingham Heart Study. Finally, I'll present an interesting perspective of the jackknife and bootstrap which would elucidate the link between the resampling and remeasurement methods.