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Two sample distribution-free inference based on partially rank-ordered set samples

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  • Gao, Jinguo
  • Ozturk, Omer

Abstract

This paper develops distribution-free inference for a location shift model based on a partially rank-ordered set (PROS) sample. In a PROS sample, a small set of experimental units is judgment ranked without measurement by allowing ties whenever the units cannot be ranked with high confidence. These tied units are replaced in partially ordered judgment subsets from which a unit is selected at random for a full measurement. Based on this sampling design, we construct an estimator, a test and a confidence interval for the location shift parameter. It is shown that the new sampling design is robust against any possible ranking error and has higher efficiency than competitor designs in the literature.

Suggested Citation

  • Gao, Jinguo & Ozturk, Omer, 2012. "Two sample distribution-free inference based on partially rank-ordered set samples," Statistics & Probability Letters, Elsevier, vol. 82(5), pages 876-884.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:5:p:876-884
    DOI: 10.1016/j.spl.2012.01.021
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    References listed on IDEAS

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    1. McIntyre, G.A., 2005. "A Method for Unbiased Selective Sampling, Using Ranked Sets," The American Statistician, American Statistical Association, vol. 59, pages 230-232, August.
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    Cited by:

    1. Jesse Frey & Timothy G. Feeman, 2017. "Efficiency comparisons for partially rank-ordered set sampling," Statistical Papers, Springer, vol. 58(4), pages 1149-1163, December.
    2. Armin Hatefi & Mohammad Jafari Jozani & Omer Ozturk, 2015. "Mixture Model Analysis of Partially Rank-Ordered Set Samples: Age Groups of Fish from Length-Frequency Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 848-871, September.

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