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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sixia Chen & David Haziza & Zeinab Mashreghi, 2022. "A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs," Stats, MDPI, vol. 5(2), pages 1-17, June.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:2:p:31-537:d:832790
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    References listed on IDEAS

    as
    1. Antal, Erika & Tillé, Yves, 2011. "A Direct Bootstrap Method for Complex Sampling Designs From a Finite Population," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 534-543.
    2. Jean‐François Beaumont & Zdenek Patak, 2012. "On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling," International Statistical Review, International Statistical Institute, vol. 80(1), pages 127-148, April.
    3. David Haziza & Fulvia Mecatti & J.N.K. Rao, 2008. "Evaluation of some approximate variance estimators under the Rao-Sampford unequal probability sampling design," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 91-108.
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