Mean-squared error estimation in transformed Fay-Herriot models
Mean-squared error estimation in transformed Fay-Herriot models
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DOI：
10.1111/j.1467-9868.2006.00542.x
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发表时间：
2006-01-01
影响因子：
5.8
通讯作者：
Maiti, T
中科院分区：
文献类型：
--
作者：
Slud, EV;Maiti, T
本文从理论上研究了Fay-Herriot正态误差模型中小区域估计量均方误差的精确估计问题。对于有偏差校正的经验最佳线性无偏预报器小面积点估计，给出了均方误差公式和估计量，其偏差阶数小于小面积数的倒数。这些均方误差估计器的性能通过一项模拟研究和一个与美国人口普查局正在进行的“小地区收入和贫困估计”项目中的儿童贫困率县级估计有关的真实数据例子来说明。
The problem of accurately estimating the mean-squared error of small area estimators within a Fay-Herriot normal error model is studied theoretically in the common setting where the model is fitted to a logarithmically transformed response variable. For bias-corrected empirical best linear unbiased predictor small area point estimators, mean-squared error formulae and estimators are provided, with biases of smaller order than the reciprocal of the number of small areas. The performance of these mean-squared error estimators is illustrated by a simulation study and a real data example relating to the county level estimation of child poverty rates in the US Census Bureau's on-going 'Small area income and poverty estimation' project.