Work

Multiply Robust Bootstrap Variance Estimation in the Presence of Singly Imputed Survey Data

Year: 2020

Type: article

Abstract: Item nonresponse in surveys is usually dealt with through single imputation. It is well known that treating the imputed values as if they were observed values may lead to serious underestimation of the variance of point estimators. In this article, we propose three pseudo-population bootstrap schemes for estimating the variance of imputed estimators obtained after applying a multiply robust imputation procedure. The proposed procedures can handle large sampling fractions and enjoy the multiple robustness property. Results from a simulation study suggest that the proposed methods perform well in terms of relative bias and coverage probability, for both population totals and quantiles.

Source: Journal of Survey Statistics and Methodology

DOI: 10.1093/jssam/smaa004

Authors: Sixia Chen, David Haziza, Zeinab Mashreghi

Institutions: University of Oklahoma Health Sciences Center, Université de Montréal, The University of Winnipeg

Language: English

Topic: Survey Sampling and Estimation Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, National Institute of General Medical Sciences

Award: G3457471677