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On Extended Neoteric Ranked Set Sampling Plan: Likelihood Function Derivation and Parameter Estimation

Author

Listed:
  • Fathy H. Riad
  • Mohamed A. Sabry
  • Ehab M. Almetwally
  • Ramy Aldallal
  • Randa Alharbi
  • Md. Moyazzem Hossain

Abstract

The extended neoteric ranked set sampling (ENRSS) plan proposed by Taconeli and Cabral has proven to outperform many one stages and two stages ranked set sampling plans when estimating the mean and the variance for different populations. Therefore, in this paper, the likelihood function based on ENRSS is proposed and used for estimation of the parameters of the inverted Nadarajah–Haghighi distribution. An extensive Monte Carlo simulation study is conducted to assess the performance of the proposed likelihood function, and the efficiency of the estimated parameters based on ENRSS is compared with the well‐known ranked set sampling (RSS) plan and some of its modifications. These modifications include the extended ranked set sampling (ERSS) plan and the neoteric ranked set sampling (NRSS) plan. The results as foreseeable were very satisfactory and gave similar results to Taconeli and Cabral’s 2019 results.

Suggested Citation

  • Fathy H. Riad & Mohamed A. Sabry & Ehab M. Almetwally & Ramy Aldallal & Randa Alharbi & Md. Moyazzem Hossain, 2022. "On Extended Neoteric Ranked Set Sampling Plan: Likelihood Function Derivation and Parameter Estimation," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:1697481
    DOI: 10.1155/2022/1697481
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    References listed on IDEAS

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    1. M. Mahdizadeh & E. Strzalkowska-Kominiak, 2017. "Resampling based inference for a distribution function using censored ranked set samples," Computational Statistics, Springer, vol. 32(4), pages 1285-1308, December.
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    Cited by:

    1. Edoh Katchekpele & Issa Cherif Geraldo & Tchilabalo Abozou Kpanzou, 2025. "Comparative Analysis and Practical Applications of Cubic Transmutations for the Pareto Distribution," International Journal of Mathematics and Mathematical Sciences, John Wiley & Sons, vol. 2025(1).

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