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Parametric estimation for the simple linear regression model under moving extremes ranked set sampling design

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Abstract

Cost effective sampling design is a major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming. Ranked set sampling (RSS) was first proposed by McIntyre [1952. A method for unbiased selective sampling, using ranked sets. Australian Journal of Agricultural Research 3, 385–390] as an effective way to estimate the pasture mean. In the current paper, a modification of ranked set sampling called moving extremes ranked set sampling (MERSS) is considered for the best linear unbiased estimators(BLUEs) for the simple linear regression model. The BLUEs for this model under MERSS are derived. The BLUEs under MERSS are shown to be markedly more efficient for normal data when compared with the BLUEs under simple random sampling.

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Authors and Affiliations

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

    Dong-sen Yao, Wang-xue Chen & Chun-xian Long

Authors
  1. Dong-sen Yao
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  2. Wang-xue Chen
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  3. Chun-xian Long
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Corresponding author

Correspondence to Wang-xue Chen.

Additional information

Supported by the National Natural Science Foundation of China(11901236), the Scientific Research Fund of Hunan Provincial Science and Technology Department(2019JJ50479), the Scientific Research Fund of Hunan Provincial Education Department(18B322), the Winning Bid Project of Hunan Province for the 4th National Economic Census([2020]1), the Young Core Teacher Foundation of Hunan Province([2020]43) and the Fundamental Research Fund of Xiangxi Autonomous Prefecture(2018SF5026).

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Yao, Ds., Chen, Wx. & Long, Cx. Parametric estimation for the simple linear regression model under moving extremes ranked set sampling design. Appl. Math. J. Chin. Univ. 36, 269–277 (2021). https://doi.org/10.1007/s11766-021-3993-1

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  • DOI: https://doi.org/10.1007/s11766-021-3993-1

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