Outlier robust small area estimation

• 成果类型: Article
• 署名作者: Chambers, Ray; Chandra, Hukum; Salvati, Nicola; Tzavidis, Nikos
• 署名单位: University of Wollongong; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; University of Pisa; University of Southampton
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12019
• 发表日期: 2014
• 页码: 47-69
• 关键词: mean squared error; Standard errors; prediction; MODEL

摘要

Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads one to consider an outlier robust bias correction for these estimators. We develop this idea, proposing two different analytical mean-squared error estimators for the ensuing bias-corrected outlier robust estimators. Simulations based on realistic outlier-contaminated data show that the bias correction proposed often leads to more efficient estimators. Furthermore, the mean-squared error estimation methods proposed appear to perform well with a variety of outlier robust small area estimators.