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Spatially Balanced Sampling of Continuous Populations

Author

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  • Anton Grafström
  • Alina Matei

Abstract

When sampling from a continuous population (or distribution), we often want a rather small sample due to some cost attached to processing the sample or to collecting information in the field. Moreover, a probability sample that allows for design‐based statistical inference is often desired. Given these requirements, we want to reduce the sampling variance of the Horvitz–Thompson estimator as much as possible. To achieve this, we introduce different approaches to using the local pivotal method for selecting well‐spread samples from multidimensional continuous populations. The results of a simulation study clearly indicate that we succeed in selecting spatially balanced samples and improve the efficiency of the Horvitz–Thompson estimator.

Suggested Citation

  • Anton Grafström & Alina Matei, 2018. "Spatially Balanced Sampling of Continuous Populations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(3), pages 792-805, September.
    • Handle: RePEc:bla:scjsta:v:45:y:2018:i:3:p:792-805
    DOI: 10.1111/sjos.12322
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    Cited by:

    1. Robertson, Blair & Price, Chris, 2024. "One point per cluster spatially balanced sampling," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
    2. Xin Zhao & Anton Grafström, 2020. "A sample coordination method to monitor totals of environmental variables," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.

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