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<p>Introducing new distributions is crucial for achieving more accurate fits to real-world data. In this context, many researchers have modified existing distributions by incorporating additional parameters. So a new distribution—called the Marshall-Olkin generalized extreme value distribution under linear normalization—is proposed and its parameters are estimated under type II progressive censoring by maximum likelihood estimation (MLE) and Bayesian estimation. However, the MLE of the original GEVL distribution cannot be obtained using classical numerical methods; so, the Whale optimization algorithm is utilized instead. Since humidity plays a vital role in the spread of forest fires, a real data set representing humidity levels in Los Angeles is investigated, showing the superiority of the proposed distribution compared to existing models. Moreover, both distribution fitting (return levels) and time series analysis are employed to forecast future humidity levels.</p>

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Parameter estimation of MO-qGEVL distribution under type II progressive censoring with application in environmental data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Tmader Alballa,
  • Said G. Nassr,
  • Ibrahim A. Fares,
  • El Husseini Mustafa Abdel Salam,
  • Shimaa Wasfy Sadk,
  • Rasha Abd El-Wahab Attwa

摘要

Introducing new distributions is crucial for achieving more accurate fits to real-world data. In this context, many researchers have modified existing distributions by incorporating additional parameters. So a new distribution—called the Marshall-Olkin generalized extreme value distribution under linear normalization—is proposed and its parameters are estimated under type II progressive censoring by maximum likelihood estimation (MLE) and Bayesian estimation. However, the MLE of the original GEVL distribution cannot be obtained using classical numerical methods; so, the Whale optimization algorithm is utilized instead. Since humidity plays a vital role in the spread of forest fires, a real data set representing humidity levels in Los Angeles is investigated, showing the superiority of the proposed distribution compared to existing models. Moreover, both distribution fitting (return levels) and time series analysis are employed to forecast future humidity levels.