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Maximum likelihood estimation of the parameters of the inverse Gaussian distribution using maximum rank set sampling with unequal samples

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  • Shuo Wang
  • Wangxue Chen
  • Meng Chen
  • Yawen Zhou

Abstract

Maximum ranked set sampling with unequal samples is a sampling procedure used to reduce the error of ranking of observations and increase the efficiency of statistical inference. It is used for maximum likelihood estimation of the location and shape parameters of the inverse Gaussian distribution. Its asymptotic efficiency is at least 1.4 times higher than those of estimators based on simple random sampling. It is useful in reliability studies and in Bayesian statistics involving the inverse Gaussian distribution.

Suggested Citation

  • Shuo Wang & Wangxue Chen & Meng Chen & Yawen Zhou, 2023. "Maximum likelihood estimation of the parameters of the inverse Gaussian distribution using maximum rank set sampling with unequal samples," Mathematical Population Studies, Taylor & Francis Journals, vol. 30(1), pages 1-21, January.
    • Handle: RePEc:taf:mpopst:v:30:y:2023:i:1:p:1-21
    DOI: 10.1080/08898480.2021.1996822
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    Cited by:

    1. Hugo Salinas & Hassan Bakouch & Najla Qarmalah & Guillermo Martínez-Flórez, 2023. "A Flexible Class of Two-Piece Normal Distribution with a Regression Illustration to Biaxial Fatigue Data," Mathematics, MDPI, vol. 11(5), pages 1-14, March.

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