A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs

Sixia Chen, David Haziza and Zeinab Mashreghi
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Sixia Chen: Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
David Haziza: Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Zeinab Mashreghi: Department of Mathematics and Statistics, University of Winnipeg, Winnipeg, MB R3B 2E9, Canada

Stats, 2022, vol. 5, issue 2, 1-17

Abstract: 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.

Keywords: bootstrap algorithms; multi-stage sampling; Taylor linearization; variance estimation (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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