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Ranked set sampling: cost and optimal set size.

Ramzi W Nahhas1, Douglas A Wolfe, Haiying Chen

  • 1Wycliffe Bible Translators, Dallas, Texas, USA.

Biometrics
|December 24, 2002
PubMed
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Ranked set sampling (RSS) can improve population mean estimation when ranking is cheaper than measurement. This study finds optimal RSS set sizes are often larger than previously thought, depending on cost structures.

Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Ranked set sampling (RSS) was introduced by McIntyre (1952) to enhance population mean estimation.
  • RSS is beneficial when sampling and ranking are less costly than direct measurement.

Purpose of the Study:

  • To analyze the impact of ranking error and cost models on optimal set sizes in RSS.
  • To determine the influence of varying cost structures on the efficiency of RSS.

Main Methods:

  • Development of ranking error models.
  • Incorporation of cost models for sampling, ranking, and measurement operations.
  • Assessment of optimal set sizes under different cost scenarios.

Main Results:

  • Optimal RSS set sizes are generally larger than previously anticipated under reasonable cost structures.

Related Experiment Videos

  • The findings provide a framework for evaluating the cost-effectiveness of RSS.

    • Conclusions:

    • The optimal set size for RSS is highly dependent on the relative costs of sampling, ranking, and measurement.
    • This research offers guidance for practitioners on when and how to best utilize RSS for improved statistical estimation.