Sindhu Tabassum Naz, Shafiq Anum, Al-Mdallal Qasem M
Department of Statistics, Quaid-i-Azam University 45320, Islamabad 44000, Pakistan.
Department of Sciences and Humanities, FAST - National University, Islamabad, Pakistan.
Results Phys. 2021 Feb;21:103747. doi: 10.1016/j.rinp.2020.103747. Epub 2020 Dec 29.
In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature.
在本文中，我们开发了一种生成器，以提出一种被称为广义Gumbel II型对数指数变换（GLET-GTII）的Gumbel II型模型的推广形式，它扩展了一种用于对寿命数据进行建模的更灵活模型。由于基本变换包含一个额外参数，通过所建议的发展，每个现有的寿命模型都可以变得更加灵活。研究了GLET-GTII的一些特定统计属性，如分位数、不确定性度量、生存函数、矩、可靠性和危险函数等。我们描述了使用最大似然估计器和贝叶斯范式讨论的GLET-GTII的两种参数估计方法。蒙特卡罗模拟分析表明估计器是一致的。进行了两个实际应用以检验我们当前策略的适用性。这些实际数据与传染病（COVID-19）相关。这些应用表明，通过使用当前方法，我们提出的模型比文献中其他知名的现有模型表现更优。
