论文标题
根据预期响应率减少样品分配的差异
Reducing Variance with Sample Allocation Based on Expected Response Rates
论文作者
论文摘要
有几种技术来评估和减少无响应偏差，包括倾向模型，校准方法或分层后。这些方法只能在数据收集之后应用，并在整个人群中采用有关单位无响应模式的可靠信息。在本文中，我们证明了在这种情况下考虑预期响应率（ERR）的样本分配具有优势。通过比较由与大小成比例（PS）的经典分配获得的估计值，然后应用后分层后获得的估计值来评估错误分配的性能。主要的理论工具是使用Delta-Method的渐近计算，并且与广泛的模拟相辅相成。主要发现是，当正确指定响应率时，并且在无法预先正确指定响应率时，错误分配会导致较低的方差高于PS分配。
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.
