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<p>Neoteric ranked set samples (NeRaSS) have been proven to outperform many ranked set sampling (RaSS) designs when making inferences about distribution parameters. Therefore, in this paper we compare the performance of two NeRSS when making statistical inference of the parameters of the generalized inverted exponential distribution. We use both maximum likelihood and Bayesian approaches to estimate the parameters of the underling distribution. The ranked samples used in this research are the usual RaSS, the usual NeRaSSs and the extended neoteric ranked set samples (ExNeRaSS). Lindley's approximation approach is utilized to generate the various designs' based Bayesian estimators during an intense Markov Chain Monte Carlo simulation analysis. The study showed that the ExNeRaSS outperforms the usual RaSS and NeRaSS where the relative efficiency (RE) and root total relative efficiency (RTRE) is more than the other designs’ RE and RTRE.</p>

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A Comparative Study of Neoteric Ranked Set Samples for the Generalized Inverted Exponential Distribution

  • 期刊论文

  • 发表日期：

    2025年3月17日

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  • Mostafa Shaaban,
  • Mohamed Sabry,
  • Abdel Tawab Ahmed

摘要

Neoteric ranked set samples (NeRaSS) have been proven to outperform many ranked set sampling (RaSS) designs when making inferences about distribution parameters. Therefore, in this paper we compare the performance of two NeRSS when making statistical inference of the parameters of the generalized inverted exponential distribution. We use both maximum likelihood and Bayesian approaches to estimate the parameters of the underling distribution. The ranked samples used in this research are the usual RaSS, the usual NeRaSSs and the extended neoteric ranked set samples (ExNeRaSS). Lindley's approximation approach is utilized to generate the various designs' based Bayesian estimators during an intense Markov Chain Monte Carlo simulation analysis. The study showed that the ExNeRaSS outperforms the usual RaSS and NeRaSS where the relative efficiency (RE) and root total relative efficiency (RTRE) is more than the other designs’ RE and RTRE.