Small area estimation when auxiliary information is measured with error
Small area estimation when auxiliary information is measured with error
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DOI：
10.1093/biomet/asn048
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发表时间：
2008-12
期刊：
影响因子：
2.7
通讯作者：
Lynn M. R. Ybarra;S. Lohr
中科院分区：
文献类型：
--
作者：
Lynn M. R. Ybarra;S. Lohr
小面积估计方法通常将调查的直接估计与模型的预测相结合，以获得均方误差较小的人口数量估计。当模型中使用的辅助信息测量有误差时，使用小面积估计器（如Fay—Herriot估计器）而忽略测量误差可能比简单使用直接估计器更糟糕。我们提出了一种新的小面积估计器，它考虑了辅助信息中的抽样变异性，并推导了它的性质，特别是表明它是近似无偏的。该估计器用于预测美国国家健康和营养检查调查中测量的数量，辅助信息来自美国国家健康访谈调查。牛津大学出版社版权所有。
Small area estimation methods typically combine direct estimates from a survey with predictions from a model in order to obtain estimates of population quantities with reduced mean squared error. When the auxiliary information used in the model is measured with error, using a small area estimator such as the Fay--Herriot estimator while ignoring measurement error may be worse than simply using the direct estimator. We propose a new small area estimator that accounts for sampling variability in the auxiliary information, and derive its properties, in particular showing that it is approximately unbiased. The estimator is applied to predict quantities measured in the U.S. National Health and Nutrition Examination Survey, with auxiliary information from the U.S. National Health Interview Survey. Copyright 2008, Oxford University Press.