简单小区域模型中有序随机效应的预测
Prediction of Ordered Random Effects in a Simple Small Area Model
摘要：
有序参数的向量或其部分的预测在小区域估计（SAE）的上下文中自然出现。例如，人们可能想要估计与前十个区域、最佳或最差区域或某个百分位数相关联的参数。我们使用一个简单的SAE模型表明，估计有序参数的相应的有序估计的每个领域分别不产生良好的结果相对于MSE。收缩型预测器，适当的收缩量的特定问题的有序参数，是相当好的，其性能接近的最佳预测器，这在一般情况下不能显式计算。
英文摘要：
Prediction of a vector of ordered parameters or part of it arises naturally in the context of Small Area Estimation (SAE). For example, one may want to estimate the parameters associated with the top ten areas, the best or worst area, or a certain percentile. We use a simple SAE model to show that estimation of ordered parameters by the corresponding ordered estimates of each area separately does not yield good results with respect to MSE. Shrinkage-type predictors, with an appropriate amount of shrinkage for the particular problem of ordered parameters, are considerably better, and their performance is close to that of the optimal predictors, which cannot in general be computed explicitly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
