Applying the rescaling bootstrap under imputation for a multistage sampling design

Christian Bruch ()
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Christian Bruch: GESIS Leibniz Institute for the Social Science

Computational Statistics, 2022, vol. 37, issue 3, No 18, 1494 pages

Abstract: Abstract In this paper, we propose a method that estimates the variance of an imputed estimator in a multistage sampling design. The method is based on the rescaling bootstrap for multistage sampling introduced by Preston (Surv Methodol 35(2):227–234, 2009). In his original version, this resampling method requires that the dataset includes only complete cases and no missing values. Thus, we propose two modifications for applying this method to nonresponse and imputation. These modifications are compared to other modifications in a Monte Carlo simulation study. The results of our simulation study show that our two proposed approaches are superior to the other modifications of the rescaling bootstrap and, in many situations, produce valid estimators for the variance of the imputed estimator in multistage sampling designs.

Keywords: Rescaling bootstrap; Variance estimation; Nonresponse; Imputation; Multistage sampling designs; Monte Carlo simulation study (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01164-6

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