文章基本信息

标题：A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs
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作者：Sixia Chen ; David Haziza ; Zeinab Mashreghi 等
期刊名称：Stats
电子版ISSN：2571-905X
出版年度：2022
卷号：5
期号：2
页码：521-537
DOI：10.3390/stats5020031
语种：English
出版社：MDPI AG
摘要：Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimation is a complex task as it relies on the availability of second-order inclusion probabilities at each stage. To cope with this issue, several bootstrap algorithms have been proposed in the literature in the context of a two-stage sampling design. In this paper, we describe some of these algorithms and compare them empirically in terms of bias, stability, and coverage probability.