A Semiparametric Correction for Attenuation
提出一种半参数校正方法，用于协变量存在测量误差的广义线性模型，通过验证数据估计条件期望并替代真实协变量，推导了参数估计的渐近分布。
Abstract A correction method is proposed for models including the generalized linear model when the covariate is measured with error. The method requires a separate validation data set that consists of the surrogate W and the true covariate X or an unbiased estimate X⊃ of X. We do not require the classical additive measurement error model in which the surrogate is unbiased for the true covariates. We first obtain an estimate of E(X|W) by using nonparametric kernel regression of X or X⊃ on W based on the validation data. Then we perform a standard analysis with the unknown X replaced by the estimate of E(X|W). The asymptotic distribution of the resulting regression parameter estimator is obtained. Generalizations to include components of X measured without error are also discussed.