Simulation-Extrapolation Estimation in Parametric Measurement Error Models
提出一种模拟外推方法，通过向数据添加已知增量的测量误差并外推至无误差情形，来估计参数测量误差模型，适用于测量误差方差已知或可准确估计的场景。
Abstract 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. Key Words: Correction for attenuationExtrapolationFramingham Heart StudyLogistic regressionMeasurement error modelMethod of momentsNonlinear modelSimulation