Multiply robust imputation procedures for the treatment of item nonresponse in surveys

• 成果类型: Article
• 署名作者: Chen, Sixia; Haziza, David
• 署名单位: University of Oklahoma System; University of Oklahoma Health Sciences Center; Universite de Montreal
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx007
• 发表日期: 2017
• 页码: 439453
• 关键词: missing data; inference; calibration; estimator

摘要

Item nonresponse in surveys is often treated through some form of imputation. We introduce multiply robust imputation in finite population sampling. This is closely related to multiple robustness, which extends double robustness. In practice, multiple nonresponse models and multiple imputation models may be fitted, each involving different subsets of covariates and possibly different link functions. An imputation procedure is said to be multiply robust if the resulting estimator is consistent when all models but one are misspecified. A jackknife variance estimator is proposed and shown to be consistent. Random and fractional imputation procedures are discussed. A simulation study suggests that the proposed estimation procedures have low bias and high efficiency.