Applying the rescaling bootstrap under imputation: a simulation study

Bruch, Christian

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Document Type: Article
Year of publication: 2019
The title of a journal, publication series: The Journal of Statistical Computation and Simulation : JSCS
Volume: 89
Issue number: 4
Page range: 641-659
Place of publication: London [u.a.]
Publishing house: Taylor & Francis
ISSN: 0094-9655 , 1563-5163
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
Subject: 300 Social sciences, sociology, anthropology
Abstract: Resampling methods are a common measure to estimate the variance of a statistic of interest when data consist of nonresponse and imputation is used as compensation. Applying resampling methods usually means that subsamples are drawn from the original sample and that variance estimates are computed based on point estimators of several subsamples. However, newer resampling methods such as the rescaling bootstrap of Chipperfield and Preston [Efficient bootstrap for business surveys. Surv Methodol. 2007;33:167–172] include all elements of the original sample in the computation of its point estimator. Thus, procedures to consider imputation in resampling methods cannot be applied in the ordinary way. For such methods, modifications are necessary. This paper presents an approach applying newer resampling methods for imputed data. The Monte Carlo simulation study conducted in the paper shows that the proposed approach leads to reliable variance estimates in contrast to other modifications.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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