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Pareto parameters estimation using moving extremes ranked set sampling

Author

Listed:
  • Wangxue Chen

    (Jishou University)

  • Rui Yang

    (Jishou University)

  • Dongsen Yao

    (Jishou University)

  • Chunxian Long

    (Jishou University)

Abstract

Cost effective sampling is a problem of major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming. In the current paper, a modification of ranked set sampling (RSS) called moving extremes RSS (MERSS) is considered for the estimation of the scale and shape parameters $$\theta $$ θ and $$\alpha $$ α from $$p(\theta , \alpha )$$ p ( θ , α ) . Several traditional estimators and ad hoc estimators will be studied under MERSS. The estimators under MERSS are compared to the corresponding ones under SRS. The simulation results show that the estimators under MERSS are significantly more efficient than the ones under SRS. A real data set is used for illustration.

Suggested Citation

  • Wangxue Chen & Rui Yang & Dongsen Yao & Chunxian Long, 2021. "Pareto parameters estimation using moving extremes ranked set sampling," Statistical Papers, Springer, vol. 62(3), pages 1195-1211, June.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:3:d:10.1007_s00362-019-01132-9
    DOI: 10.1007/s00362-019-01132-9
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    References listed on IDEAS

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    Cited by:

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    3. Heba F. Nagy & Amer Ibrahim Al-Omari & Amal S. Hassan & Ghadah A. Alomani, 2022. "Improved Estimation of the Inverted Kumaraswamy Distribution Parameters Based on Ranked Set Sampling with an Application to Real Data," Mathematics, MDPI, vol. 10(21), pages 1-19, November.

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