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Improved unbiased estimators in adaptive cluster sampling

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  • Arthur L. Dryver
  • Steven K. Thompson

Abstract

Summary. The usual design‐unbiased estimators in adaptive cluster sampling are easy to compute but are not functions of the minimal sufficient statistic and hence can be improved. Improved unbiased estimators obtained by conditioning on sufficient statistics—not necessarily minimal—are described. First, estimators that are as easy to compute as the usual design‐unbiased estimators are given. Estimators obtained by conditioning on the minimal sufficient statistic which are more difficult to compute are also discussed. Estimators are compared in examples.

Suggested Citation

  • Arthur L. Dryver & Steven K. Thompson, 2005. "Improved unbiased estimators in adaptive cluster sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 157-166, February.
    • Handle: RePEc:bla:jorssb:v:67:y:2005:i:1:p:157-166
    DOI: 10.1111/j.1467-9868.2005.00493.x
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    Cited by:

    1. repec:jss:jstsof:31:c03 is not listed on IDEAS
    2. Raosaheb V. Latpate & Jayant K. Kshirsagar, 2020. "Two Stage Inverse Adaptive Cluster Sampling With Stopping Rule Depends upon the Size of Cluster," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 70-83, May.
    3. Steven Thompson, 2013. "Adaptive web sampling in ecology," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 33-43, March.
    4. Stefano Gattone & Tonio Di Battista, 2011. "Adaptive cluster sampling with a data driven stopping rule," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 1-21, March.
    5. Dryver, Arthur, 2009. "The Enhancement of Teaching Materials for Applied Statistics Courses by Combining Random Number Generation and Portable Document Format Files via LaTeX," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(c03).

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