A correctly specified working correlation structure ensures the efficiency in inferences for marginal regressions based on estimating equations. When there are measurement errors in covariates, however, correct specification of the working correlation structure may lead to more severe bias and higher mean squared error than working independence. We report this lesser known phenomenon and explain it in a bias analysis. The bias can be corrected using a functional approach with efficiency improved through the generalized method of moments, which works well only for large samples. For practical purposes, we further address two computational issues and correct the variance estimator for small samples. The proposed approaches are validated in simulation studies and illustrated in an example. The methods are publicly available in an R package eiv.

中文翻译：

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测量误差下广义估计方程的偏差分析及实际偏差修正

正确指定的工作相关结构确保了基于估计方程的边际回归推理的效率。然而，当协变量中存在测量误差时，正确指定工作相关结构可能会导致比工作独立性更严重的偏差和更高的均方误差。我们报告了这种鲜为人知的现象，并在偏差分析中对其进行了解释。可以使用函数方法来纠正偏差，通过矩量的广义方法提高效率，该方法仅适用于大样本。出于实际目的，我们进一步解决了两个计算问题并纠正了小样本的方差估计。所提出的方法在模拟研究中得到验证，并在示例中进行了说明。这些方法在 R 包中公开可用iv .