  For the general parametric regression models with covariates contaminated
with normal measurement errors, this paper proposes an accelerated version of
the classical simulation extrapolation algorithm to estimate the unknown
parameters in the regression function. By applying the conditional expectation
directly to the target function, the proposed algorithm successfully removes
the simulation step, by generating an estimation equation either for immediate
use or for extrapolating, thus significantly reducing the computational time.
Large sample properties of the resulting estimator, including the consistency
and the asymptotic normality, are thoroughly discussed. Potential wide
applications of the proposed estimation procedure are illustrated by examples,
simulation studies, as well as a real data analysis.
