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.

翻译：对于具有受正常测量误差污染的共差的普通参数回归模型,本文件建议加速采用经典模拟外推算法,以估计回归函数中未知参数。通过将有条件的预期直接应用到目标函数中,拟议的算法成功地消除了模拟步骤,生成了用于立即使用或外推的估计方程,从而大大缩短了计算时间。由此得出的估计值的大量样本性质,包括一致性和无症状的正常性,都得到了透彻的讨论。通过实例、模拟研究以及真实的数据分析,说明了拟议估算程序的潜在广泛应用。