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<p>This paper develops a Bayesian analysis in the context of flexible ranked set sampling from the one-parameter Rayleigh distribution. The Bayes estimates of the unknown parameter are obtained under general entropy loss and Al-Bayyati loss functions. Some methods are used to construct the corresponding credible intervals, namely the Bayes credible intervals, highest posterior density (HPD) intervals, and Markov Chain Monte Carlo (MCMC) techniques. We further consider two-sample Bayes prediction (either point or interval) problems based on the observed sample. To compare and illustrate the results derived, Monte Carlo simulations are performed, and a real data set is examined.</p>

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Bayesian Inference and Prediction of the Rayleigh Distribution for Flexible Ranked Set Sampling

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Mohammed S. Kotb,
  • Huda M. Alomari

摘要

This paper develops a Bayesian analysis in the context of flexible ranked set sampling from the one-parameter Rayleigh distribution. The Bayes estimates of the unknown parameter are obtained under general entropy loss and Al-Bayyati loss functions. Some methods are used to construct the corresponding credible intervals, namely the Bayes credible intervals, highest posterior density (HPD) intervals, and Markov Chain Monte Carlo (MCMC) techniques. We further consider two-sample Bayes prediction (either point or interval) problems based on the observed sample. To compare and illustrate the results derived, Monte Carlo simulations are performed, and a real data set is examined.