A unified approach to robust estimation in finite population sampling
成果类型：
Article
署名作者：
Beaumont, J. -F.; Haziza, D.; Ruiz-Gazen, A.
署名单位：
Statistics Canada; Universite de Montreal; Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/ast010
发表日期：
2013
页码：
555569
关键词：
摘要：
We argue that the conditional bias associated with a sample unit can be a useful measure of influence in finite population sampling. We use the conditional bias to derive robust estimators that are obtained by downweighting the most influential sample units. Under the model-based approach to inference, our proposed robust estimator is closely related to the well-known estimator of Chambers (1986). Under the design-based approach, it possesses the desirable feature of being applicable with most sampling designs used in practice. For stratified simple random sampling, it is essentially equivalent to the estimator of Kokic & Bell (1994). The proposed robust estimator depends on a tuning constant. In this paper, we propose a method for determining the tuning constant and show that the resulting estimator is consistent. Results from a simulation study suggest that our approach improves the efficiency of standard nonrobust estimators when the population contains units that may be influential if selected in the sample.