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Tests of perfect judgment ranking using pseudo-samples

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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.

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Acknowledgments

We gratefully acknowledge the constructive comments and suggestions of the anonymous referee, and the associate editor.

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Authors and Affiliations

  1. Department of Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, WI, USA

    Saeid Amiri

  2. Department of Statistics, The George Washington University, Washington, DC, USA

    Reza Modarres

  3. Department of Mathematics, Uppsala University, Uppsala, Sweden

    Silvelyn Zwanzig

Authors
  1. Saeid Amiri
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  2. Reza Modarres
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  3. Silvelyn Zwanzig
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Correspondence to Saeid Amiri.

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Amiri, S., Modarres, R. & Zwanzig, S. Tests of perfect judgment ranking using pseudo-samples. Comput Stat 32, 1309–1322 (2017). https://doi.org/10.1007/s00180-016-0698-7

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  • DOI: https://doi.org/10.1007/s00180-016-0698-7

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