Mean-squared error estimation in transformed Fay-Herriot models

• 成果类型: Article
• 署名作者: Slud, EV; Maiti, T
• 署名单位: University System of Maryland; University of Maryland College Park; Iowa State University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2006.00542.x
• 发表日期: 2006
• 页码: 239-257
• 关键词: small-area estimators

摘要

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.