SMALL-AREA ESTIMATION - AN APPRAISAL
SMALL-AREA ESTIMATION - AN APPRAISAL
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DOI：
10.1214/ss/1177010647
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发表时间：
1994-02-01
影响因子：
5.7
通讯作者：
RAO, JNK
中科院分区：
文献类型：
--
作者：
GHOSH, M;RAO, JNK
由于公共和私营部门对可靠的小区域统计数据的需求日益增长，小区域估计在调查抽样中变得越来越重要。现在人们普遍认识到，对小地区的直接调查估计，由于这些地区的样本量较小，很可能产生难以接受的大标准误差。这使得有必要从相关领域“借用力量”，为给定领域或同时为几个领域找到更准确的估计。这导致了替代方法的发展，如合成、样本量依赖、经验最佳线性无偏预测、经验贝叶斯和层次贝叶斯估计。本文主要是对其中一些方法的评价。这些方法的性能还使用一些类似于业务人口的合成数据进行评估。经验最佳线性无偏预测以及经验贝叶斯和层次贝叶斯，在大多数情况下，似乎比其他方法有明显的优势。
Small area estimation is becoming important in survey sampling due to a growing demand for reliable small area statistics from both public and private sectors. It is now widely recognized that direct survey estimates for small areas are likely to yield unacceptably large standard errors due to the smallness of sample sizes in the areas. This makes it necessary to ''borrow strength'' from related areas to find more accurate estimates for a given area or, simultaneously, for several areas. This has led to the development of alternative methods such as synthetic, sample size dependent, empirical best linear unbiased prediction, empirical Bayes and hierarchical Bayes estimation. The present article is largely an appraisal of some of these methods. The performance of these methods is also evaluated using some synthetic data resembling a business population. Empirical best linear unbiased prediction as well as empirical and hierarchical Bayes, for most purposes, seem to have a distinct advantage over other methods.