论文标题
样品调查中无反应下的模型辅助估计器
Model-Assisted Estimators under Nonresponse in Sample Surveys
论文作者
论文摘要
在存在辅助信息的情况下，模型辅助估计器使用一个工作模型，该模型将感兴趣的变量和辅助变量链接起来，以改善Horvitz-Thompson估计器。在某些规律性条件和广泛的工作模型下，所得估计量比Horvitz-Thompson估计量渐近设计公正和渐近效率。在这项工作中，我们将模型辅助的总估计器调整为在随机数据构建中缺少关于无响应权重调整的想法的缺失。我们将无响应视为调查的第二阶段，并使用估计响应概率的倒数来将单位单位重新为单位，以补偿非响应。我们发展了渐近性能，并讨论了我们提出的估计量的重量的校准。我们为渐近方差和方差估计器提供公式。我们进行了一项仿真研究，描述了提出的估计量的行为。
In the presence of auxiliary information, model-assisted estimators use a working model that links the variable of interest and the auxiliary variables in order to improve the Horvitz-Thompson estimator. The resulting estimators are asymptotically design unbiased and asymptotically more efficient than the Horvitz-Thompson estimator under some regularity conditions and for a wide range of working models. In this work, we adapt model-assisted total estimators to missing at random data building on the idea of nonresponse weighting adjustment. We consider nonresponse as a second phase of the survey and reweight the units in model-assisted estimators using the inverse of estimated response probabilities in order to compensate for the nonrespondents. We develop the asymptotic properties and discuss calibration of the weights of our proposed estimators. We provide formulae for asymptotic variance and variance estimators. We conduct a simulation study that describes the behavior of the proposed estimators.
