SIMULATION-EXTRAPOLATION ESTIMATION IN PARAMETRIC MEASUREMENT ERROR MODELS
SIMULATION-EXTRAPOLATION ESTIMATION IN PARAMETRIC MEASUREMENT ERROR MODELS
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DOI：
10.2307/2290994
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发表时间：
1994-12-01
影响因子：
3.7
通讯作者：
STEFANSKI, LA
中科院分区：
文献类型：
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作者：
COOK, JR;STEFANSKI, LA
我们描述了一种基于仿真的参数测量误差模型的推理方法，其中测量误差方差是已知的或至少是很好的估计。该方法需要在已知增量的数据中添加额外的测量误差，从受污染的数据中计算估计值，在这些估计值和添加误差的方差之间建立趋势，并将这种趋势外推到没有测量误差的情况下。结果表明，该方法与线性测量误差建模中的矩估计方法等价或渐等效。仿真研究表明，该方法在标准和非标准逻辑回归模型中产生的估计量几乎是渐近无偏的，并且是有效的。对该方法的一个过于简化但相当准确的描述是，它是使用蒙特卡罗导出的估计方程的矩量估计方法。
We describe a simulation-based method of inference for parametric measurement error models in which the measurement error variance is known or at least well estimated. The method entails adding additional measurement error in known increments to the data, computing estimates from the contaminated data, establishing a trend between these estimates and the variance of the added errors, and extrapolating this trend back to the case of no measurement error. We show that the method is equivalent or asymptotically equivalent to method-of-moments estimation in linear measurement error modeling. Simulation studies are presented showing that the method produces estimators that are nearly asymptotically unbiased and efficient in standard and nonstandard logistic regression models. An oversimplified but fairly accurate description of the method is that it is method-of-moments estimation using Monte Carlo-derived estimating equations.