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A unified approach to variations of ranked set sampling with applications

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  • Kaushik Ghosh
  • Ram Tiwari

Abstract

In this article, we develop a general theory of inference using data collected from different variations of ranked set sampling. Such variations include balanced and unbalanced ranked set sampling, balanced and unbalanced k-tuple ranked set sampling, nomination sampling, simple random sampling, as well as a combination of them. We provide methods of estimating the underlying distribution function as well as its functionals and establish the asymptotic properties of the resulting estimators. The results so obtained can be used to develop nonparametric procedures for one- and two-sample problems. Some investigation of the small-sample properties of these estimators is also provided. We conclude with an application to a real-life example.

Suggested Citation

  • Kaushik Ghosh & Ram Tiwari, 2009. "A unified approach to variations of ranked set sampling with applications," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(4), pages 471-485.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:4:p:471-485
    DOI: 10.1080/10485250802652077
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    References listed on IDEAS

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    1. You-Gan Wang & Zehua Chen & Jianbin Liu, 2004. "General Ranked Set Sampling with Cost Considerations," Biometrics, The International Biometric Society, vol. 60(2), pages 556-561, June.
    2. Ramzi W. Nahhas & Douglas A. Wolfe & Haiying Chen, 2002. "Ranked Set Sampling: Cost and Optimal Set Size," Biometrics, The International Biometric Society, vol. 58(4), pages 964-971, December.
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    Cited by:

    1. Mohammad Nourmohammadi & Mohammad Jafari Jozani & Brad C. Johnson, 2020. "Parametric Inference Using Nomination Sampling with an Application to Mercury Contamination in Fish," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 115-146, February.
    2. Xinlei Wang & Ke Wang & Johan Lim, 2012. "Isotonized CDF Estimation from Judgment Poststratification Data with Empty Strata," Biometrics, The International Biometric Society, vol. 68(1), pages 194-202, March.

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