Mathematics and Statistics, The University of Dodoma, Dodoma, 338, Tanzania.
F1000Res. 2023 Nov 21;11:1444. doi: 10.12688/f1000research.127363.3. eCollection 2022.
The creation of developing new generalized classes of distributions has attracted applied and theoretical statisticians owing to their properties of flexibility. The development of generalized distribution aims to find distribution flexibility and suitability for available data. In this decade, most authors have developed classes of distributions that are new, to become valuable for applied researchers.
This study aims to develop the odd log-logistic generalized exponential distribution (OLLGED), one of the lifetime newly generated distributions in the field of statistics. The advantage of the newly generated distribution is the heavily tailed distributed lifetime data set. Most of the probabilistic properties are derived including generating functions, moments, and quantile and order statistics.
Estimation of the model parameter is done by the maximum likelihood method. The performance of parametric estimation is studied through simulation. Application of OLLGED and its flexibilities is done using two data sets and while its performance is done on the randomly simulated data set.
The application and flexibility of the OLLGED are ensured through empirical observation using two sets of lifetime data, establishing that the proposed OLLGED can provide a better fit in comparison to existing rival models, such as odd generalized log-logistic, type-II generalized log-logistic, exponential distributions, odd exponential log-logistic, generalized exponential, and log-logistic.
由于灵活性的特点,新的广义分布类别的创建吸引了应用和理论统计学家。广义分布的发展旨在寻找分布的灵活性和对可用数据的适用性。在这十年中,大多数作者已经开发了新的分布类,以成为应用研究人员的宝贵资源。
本研究旨在开发奇数对数 - 逻辑广义指数分布(OLLGED),这是统计学领域中最新生成的分布之一。新生成的分布的优点是具有重尾分布的寿命数据集。推导了大多数概率性质,包括生成函数、矩、分位数和顺序统计量。
通过最大似然法进行模型参数估计。通过模拟研究参数估计的性能。使用两个数据集来应用 OLLGED 及其灵活性,并在随机模拟数据集上进行性能评估。
通过使用两组寿命数据进行实证观察,确保了 OLLGED 的应用和灵活性,证明了与现有竞争模型(如奇数广义对数 - 逻辑、II 型广义对数 - 逻辑、指数分布、奇数指数对数 - 逻辑、广义指数和对数 - 逻辑)相比,提出的 OLLGED 可以提供更好的拟合。