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Uniform k-Tuple Partially Rank-Ordered Set Sampling

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  • Marvin Javier
  • Kaushik Ghosh

Abstract

Ranked Set Sampling (RSS), introduced by McIntyre, and other related methods, such as Partially Rank-Ordered Set Sampling (PROSS), have shown that inclusion of a ranking mechanism produces estimators with lower variance than their simple random sample (SRS)-based counterparts. Like RSS, PROSS takes only one measurement from each partially ranked-ordered set. We propose a sampling plan called Uniform k-Tuple Partially Rank-Ordered (UKPRSS) where a measurement is collected from each group of a partially rank-ordered set. This article demonstrates estimators from UKPRSS have lower variance than their SRS counterparts. In addition, there is a reduction in the number units needing to be screened when compared to PROSS. Estimation of the mean and distribution function are investigated theoretically.

Suggested Citation

  • Marvin Javier & Kaushik Ghosh, 2023. "Uniform k-Tuple Partially Rank-Ordered Set Sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(7), pages 2338-2355, April.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:7:p:2338-2355
    DOI: 10.1080/03610926.2021.1952266
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