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Efficiency bounds for a generalization of ranked-set sampling

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  • Timothy G. Feeman
  • Jesse Frey

Abstract

Partially rank-ordered set sampling (PROSS) is a generalization of ranked-set sampling (RSS) in which the ranker is not required to give a full ranking in each set. In this paper, we compare the efficiency of the sample mean as an estimator of the population mean under PROSS, RSS, and simple random sampling (SRS). We find that for fixed set size and total sample size, the efficiency of PROSS falls between that of SRS and that of RSS. We also develop a method for finding a sharp upper bound on the efficiency of PROSS relative to SRS for a particular design.

Suggested Citation

  • Timothy G. Feeman & Jesse Frey, 2016. "Efficiency bounds for a generalization of ranked-set sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(3), pages 739-756, February.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:3:p:739-756
    DOI: 10.1080/03610926.2013.835418
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    Cited by:

    1. Jesse Frey & Timothy G. Feeman, 2017. "Efficiency comparisons for partially rank-ordered set sampling," Statistical Papers, Springer, vol. 58(4), pages 1149-1163, December.

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