Small area estimation when auxiliary information is measured with error

• 成果类型: Article
• 署名作者: Ybarra, Lynn M. R.; Lohr, Sharon L.
• 署名单位: Arizona State University; Arizona State University-Tempe
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn048
• 发表日期: 2008
• 页码: 919931
• 关键词: prediction

摘要

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.