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Tests of perfect judgment ranking using pseudo-samples

Author

Listed:
  • Saeid Amiri

    (University of Wisconsin-Green Bay)

  • Reza Modarres

    (The George Washington University)

  • Silvelyn Zwanzig

    (Uppsala University)

Abstract

Ranked set sampling (RSS) is a sampling approach that can produce improved statistical inference when the ranking process is perfect. While some inferential RSS methods are robust to imperfect rankings, other methods may fail entirely or provide less efficiency. We develop a nonparametric procedure to assess whether the rankings of a given RSS are perfect. We generate pseudo-samples with a known ranking and use them to compare with the ranking of the given RSS sample. This is a general approach that can accommodate any type of raking, including perfect ranking. To generate pseudo-samples, we consider the given sample as the population and generate a perfect RSS. The test statistics can easily be implemented for balanced and unbalanced RSS. The proposed tests are compared using Monte Carlo simulation under different distributions and applied to a real data set.

Suggested Citation

  • Saeid Amiri & Reza Modarres & Silvelyn Zwanzig, 2017. "Tests of perfect judgment ranking using pseudo-samples," Computational Statistics, Springer, vol. 32(4), pages 1309-1322, December.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:4:d:10.1007_s00180-016-0698-7
    DOI: 10.1007/s00180-016-0698-7
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    References listed on IDEAS

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    1. Frey, Jesse & Ozturk, Omer & Deshpande, Jayant V., 2007. "Nonparametric Tests for Perfect Judgment Rankings," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 708-717, June.
    2. Frey, Jesse & Wang, Le, 2013. "Most powerful rank tests for perfect rankings," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 157-168.
    3. Ehsan Zamanzade & Nasser Reza Arghami & Michael Vock, 2014. "A Parametric Test of Perfect Ranking in Balanced Ranked Set Sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(21), pages 4589-4611, November.
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    Cited by:

    1. Cesar Augusto Taconeli & Suely Ruiz Giolo, 2020. "Maximum likelihood estimation based on ranked set sampling designs for two extensions of the Lindley distribution with uncensored and right-censored data," Computational Statistics, Springer, vol. 35(4), pages 1827-1851, December.

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