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Fast balanced sampling for highly stratified population

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  • Hasler, Caren
  • Tillé, Yves

Abstract

Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. A procedure to select balanced samples in a stratified population has previously been proposed. Unfortunately, this procedure becomes very slow as the number of strata increases and it even fails to select samples for some large numbers of strata. A new algorithm to select balanced samples in a stratified population is proposed. This new procedure is much faster than the existing one when the number of strata is large. Furthermore, this new procedure makes it possible to select samples for some large numbers of strata, which was impossible with the existing method. Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum. Finally, this algorithm turns out to be valuable for many applications as, for instance, for the handling of nonresponse.

Suggested Citation

  • Hasler, Caren & Tillé, Yves, 2014. "Fast balanced sampling for highly stratified population," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 81-94.
    • Handle: RePEc:eee:csdana:v:74:y:2014:i:c:p:81-94
    DOI: 10.1016/j.csda.2013.12.005
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    References listed on IDEAS

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    Cited by:

    1. Chauvet, Guillaume & Do Paco, Wilfried, 2018. "Exact balanced random imputation for sample survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 1-16.
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    3. Leuenberger, Michael & Eustache, Esther & Jauslin, Raphaël & Tillé, Yves, 2022. "Balancing a sample almost perfectly," Statistics & Probability Letters, Elsevier, vol. 180(C).
    4. 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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