A Rescaling Bootstrap Approach For Imputed Survey Data. (19th February 2022)

• Record Type: Journal Article
• Authors: Mashreghi, Zeinab; Deng, Huiqi
• Is Part Of: Journal of Survey Statistics and Methodology. Volume 11:Number 1(2023)
• Page Start: 234
• Page End: 259
• Publication Date: 2022-02-19
• Subjects: Imputed survey data -- Large sampling fractions -- Rescaling bootstrap approach -- Systems of linear equations -- Unequal response probabilities -- Variance estimation; Surveys -- Methodology -- Periodicals; Surveys -- Evaluation -- Periodicals; Sampling (Statistics) -- Periodicals; 001.433
• DOI: 10.1093/jssam/smab047
• Languages: English
• ISSNs: 2325-0984

Abstract

Abstract: Imputation is usually used to deal with item nonresponse in surveys. Treating the imputed values as true observations may obviously lead to serious underestimation of the variance of point estimators. In this article, we propose a new bootstrap method under the rescaling bootstrap approach for estimating the variance of an imputed estimator obtained after applying deterministic regression or random hot-deck imputation. A novel technique is used to rescale the original data set through solving certain systems of linear equations. The proposed procedure can handle unequal response probabilities and large sampling fractions. Some simulation studies are conducted to show the great performance of the proposed method in terms of relative bias, relative efficiency, and coverage probability, for both population mean and median.