The estimation of the mean squared error of small-area estimators
The estimation of the mean squared error of small-area estimators
复制标题
DOI：
10.1080/01621459.1990.10475320
复制
发表时间：
1990-03
影响因子：
3.7
通讯作者：
N. Prasad;J. Rao
中科院分区：
文献类型：
--
作者：
N. Prasad;J. Rao
摘要 由于对可靠小区域统计的需求不断增长，小区域估计近年来受到了相当大的关注。仅基于给定小区域（或小域）的数据的直接调查估计器，由于域中的样本量较小，可能会产生不可接受的大标准误差。因此，文献中提出了借鉴其他相关小领域力量的替代估计器来提高效率。这些估计者使用隐式或显式的模型，通过补充（例如人口普查和行政）数据将小区域连接起来。例如，简单的综合估计器基于隐式建模。在本文中，研究了 Battese、Harter 和 Fuller (1988)、Dempster、Rubin 和 Tsutakawa (1981) 以及 Fay 和 Herriot (1979) 的三个小区域模型。这些模型都是涉及固定效应和随机效应的一般混合线性模型的特例，并且可以表达小区域均值...
Abstract Small-area estimation has received considerable attention in recent years because of a growing demand for reliable small-area statistics. The direct-survey estimators, based only on the data from a given small area (or small domain), are likely to yield unacceptably large standard errors because of small sample size in the domain. Therefore, alternative estimators that borrow strength from other related small areas have been proposed in the literature to improve the efficiency. These estimators use models, either implicitly or explicitly, that connect the small areas through supplementary (e.g., census and administrative) data. For example, simple synthetic estimators are based on implicit modeling. In this article, three small-area models, of Battese, Harter, and Fuller (1988), Dempster, Rubin, and Tsutakawa (1981), and Fay and Herriot (1979), are investigated. These models are all special cases of a general mixed linear model involving fixed and random effects, and a small-area mean can be expr...