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<p>The main goal of this paper is to analyze the stress-strength parameter under the hybrid progressive censored scheme for the monthly water capacity of the Shasta reservoir data in California, USA. For this, we consider the stress and strength as two independent random variables of Gompertz and give the Bayesian and non-Bayesian estimates in three cases as the theoretical results. Furthermore, we apply the Monte Carlo simulations for comparing the performances of different methods. Then, with respect to a special scenario, we construct the asymptotic and highest posterior density (HPD) intervals for this parameter. Finally, we observe the results based on the Bayesian estimations are more preferred, in comparison with the others and we would like to use the HPD credible intervals as the best intervals for the monthly water capacity of the Shasta reservoir data.</p>

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Stress-strength Gompertz analysis with application to the monthly water capacity of the Shasta reservoir data

  • 期刊论文

  • 发表日期：

    2025年2月22日

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  • Alimohammad Beiranvand,
  • Ramin Kazemi,
  • Akram Kohansal,
  • Farshin Hormozinejad,
  • Mohammad Reza Ghalani

摘要

The main goal of this paper is to analyze the stress-strength parameter under the hybrid progressive censored scheme for the monthly water capacity of the Shasta reservoir data in California, USA. For this, we consider the stress and strength as two independent random variables of Gompertz and give the Bayesian and non-Bayesian estimates in three cases as the theoretical results. Furthermore, we apply the Monte Carlo simulations for comparing the performances of different methods. Then, with respect to a special scenario, we construct the asymptotic and highest posterior density (HPD) intervals for this parameter. Finally, we observe the results based on the Bayesian estimations are more preferred, in comparison with the others and we would like to use the HPD credible intervals as the best intervals for the monthly water capacity of the Shasta reservoir data.