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Tests of perfect judgment ranking using pseudo-samples
Univ Wisconsin, Dept Nat & Appl Sci, Green Bay, WI 54302 USA..
George Washington Univ, Dept Stat, Washington, DC 20052 USA..
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
2017 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 32, no 4, p. 1309-1322Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 32, no 4, p. 1309-1322
Keywords [en]
Imperfect rankings, Order statistics, Ranked set sampling, Resampling
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-339732DOI: 10.1007/s00180-016-0698-7ISI: 000413025300004OAI: oai:DiVA.org:uu-339732DiVA, id: diva2:1177890
Available from: 2018-01-26 Created: 2018-01-26 Last updated: 2018-01-26Bibliographically approved

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Zwanzig, Silvelyn

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