Pseudo-population bootstrap methods for imputed survey data

• 成果类型: Article
• 署名作者: Chen, S.; Haziza, D.; Leger, C.; Mashreghi, Z.
• 署名单位: University of Oklahoma System; University of Oklahoma Health Sciences Center; Universite de Montreal; University of Winnipeg
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asz001
• 发表日期: 2019
• 页码: 369384
• 关键词: doubly robust inference; variance-estimation; Asymptotic Normality; sampling designs; Missing Data; probabilities; imputation; regression

摘要

The most common way to treat item nonresponse in surveys is to replace a missing value by a plausible value constructed on the basis of fully observed variables. Treating the imputed values as if they were observed may lead to invalid inferences. Bootstrap variance estimators for various finite population parameters are obtained using two pseudo-population bootstrap schemes. We establish the asymptotic properties of the resulting bootstrap variance estimators for population totals and population quantiles. A simulation study suggests that the methods perform well in terms of relative bias and coverage probability.