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Bootstrap Variance Estimation for Rejective Sampling

Author

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  • Wayne A. Fuller
  • Jason C. Legg
  • Yang Li

Abstract

Replication procedures have proven useful for variance estimation for large scale complex surveys. As an extension of bootstrap procedures to rejective samples, we define a bootstrap sample that is a rejective, unequal probability, replacement sample selected from the original sample. A modification of the bootstrap with improved performance is suggested for stratified samples with small stratum sizes. Simulations for Poisson and stratified rejective samples support the use of replicates in estimating the variance of the regression estimator for rejective samples.

Suggested Citation

  • Wayne A. Fuller & Jason C. Legg & Yang Li, 2017. "Bootstrap Variance Estimation for Rejective Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1562-1570, October.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:520:p:1562-1570
    DOI: 10.1080/01621459.2016.1222285
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Stefan Marius & Hidiroglou Michael A., 2020. "A Procedure for Estimating the Variance of the Population Mean in Rejective Sampling," Journal of Official Statistics, Sciendo, vol. 36(1), pages 173-196, March.
    2. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.

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