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Best Linear Unbiased Estimators of Location and Scale Ranked Set Parameters under Moving Extremes Sampling Design

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Abstract

In the current paper, the best linear unbiased estimators (BLUEs) of location and scale parameters from location-scale family will be respectively proposed in cases when one parameter is known and when both are unknown under moving extremes ranked set sampling (MERSS). Explicit mathematical expressions of these estimators and their variances are derived. Their relative efficiencies with respect to the minimum variance unbiased estimators (MVUEs) under simple random sampling (SRS) are compared for the cases of some usual distributions. The numerical results show that the BLUEs under MERSS are significantly more efficient than the MVUEs under SRS.

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Acknowledgments

The authors thank the Editor in Chief, an associate editor and reviewers for their valuable comments and suggestions to improve the paper.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Jishou University, Jishou, 416000, China

    Yan-fei Dong & Wang-xue Chen

  2. Department of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, China

    Min-yu Xie

Authors
  1. Yan-fei Dong
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  2. Wang-xue Chen
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  3. Min-yu Xie
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Corresponding author

Correspondence to Wang-xue Chen.

Additional information

This paper is supported by the This research was supported by National Science Foundation of China (Grant Nos. 12261036 and 11901236), Scientific Research Fund of Hunan Provincial Education Department (Grant No.21A0328), Provincial Natural Science Foundation of Hunan (Grant No.2022JJ30469) and Young Core Teacher Foundation of Hunan Province (Grant No. [2020]43).

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Dong, Yf., Chen, Wx. & Xie, My. Best Linear Unbiased Estimators of Location and Scale Ranked Set Parameters under Moving Extremes Sampling Design. Acta Math. Appl. Sin. Engl. Ser. 39, 222–231 (2023). https://doi.org/10.1007/s10255-023-1043-x

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  • DOI: https://doi.org/10.1007/s10255-023-1043-x

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