Survey calibration (or generalized raking) estimators are a standard approach to the use of auxiliary information in survey sampling, improving on the simple Horvitz-Thompson estimator. In this paper we relate the survey calibration estimators to the semiparametric incomplete-data estimators of Robins and coworkers, and to adjustment for baseline variables in a randomized trial. The development based on calibration estimators explains the 'estimated weights' paradox and provides useful heuristics for constructing practical estimators. We present some examples of using calibration to gain precision without making additional modelling assumptions in a variety of regression models.

中文翻译：

---

不完全数据的测量校准估计量和半参数模型之间的联系

调查校准（或广义倾斜）估计量是在调查抽样中使用辅助信息的标准方法，改进了简单的 Horvitz-Thompson 估计量。在本文中，我们将调查校准估计量与 Robins 和同事的半参数不完全数据估计量以及随机试验中的基线变量调整联系起来。基于校准估计器的开发解释了“估计权重”悖论，并为构建实用估计器提供了有用的启发式方法。我们提供了一些使用校准来获得精度的示例，而无需在各种回归模型中进行额外的建模假设。