基于期望响应率的样本分配方法减小方差
Reducing Variance with Sample Allocation Based on Expected Response Rates
摘要：
有几种技术可以评估和减少无应答偏倚，包括倾向模型、校准方法或后分层。这些方法只能在数据收集后应用，并假设整个人群的单位无应答模式的可靠信息。在本文中，我们证明了样本分配考虑到预期响应率（ERR）在这方面的优势。ERR分配的性能进行了评估，通过比较的方差估计所获得的经典分配比例的大小（PS），然后应用后分层。主要的理论工具是渐近计算使用三角洲的方法，这些都是补充广泛的模拟。主要的发现是，ERR分配导致较低的方差比PS分配，当响应率被正确指定时，也在很大范围的条件下，当响应率不能被正确指定提前。
英文摘要：
Several techniques exist to assess and reduce nonresponse bias, including propensity models, calibration methods, or post-stratification. These approaches can only be applied after the data collection, and assume reliable information regarding unit nonresponse patterns for the entire population. In this paper, we demonstrate that sample allocation taking into account the expected response rates (ERR) have advantages in this context. The performance of ERR allocation is assessed by comparing the variances of estimates obtained those arising from a classical allocation proportional to size (PS) and then applying post-stratification. The main theoretical tool is asymptotic calculations using the delta-method, and these are complemented with extensive simulations. The main finding is that the ERR allocation leads to lower variances than the PS allocation, when the response rates are correctly specified, and also under a wide range of conditions when the response rates can not be correctly specified in advance.
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