对于离散的Poisson Ramos-Louzada分布的经典和贝叶斯推理,适用于COVID-19数据
1Department of Mathematics, Al-Qunfudah University College, Umm Al-Qura University, Mecca, Saudi Arabia.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
一个新的Poisson分布扩展,Ramos-Louzada (RL) 分布,提供了改进的统计和可靠性属性. 这种新型模型在实际应用中显示出与现有的离散分布相比,性能优越.
科学领域:
- 统计 统计 统计 统计
- 可能性理论概率理论.
- 离散的分布 离散的分布
背景情况:
- 波桑分布是一个基本的离散概率分布,广泛用于统计建模.
- 扩展现有发行版对于提高建模能力和解决更简单模型的局限性至关重要.
- 拉莫斯-卢扎达分布为开发新的统计分布提供了一个灵活的框架.
研究的目的:
- 引入并推导出基于拉莫斯-卢扎达分布的Poisson分布的新扩展.
- 调查拟议分布的统计和可靠性属性.
- 评估新模型的性能与现有的离散分布相比.
主要方法:
- 统计属性的导出:因数时刻,时刻生成函数,概率时刻,斜率,曲率和分散指数.
- 模型参数的估计使用经典技术 (例如,最大概率) 和贝叶斯估计与马先.
- 一个模拟研究来比较不同估计方法的效率.
- 将新模型应用于用于绩效评估的现实数据.
主要成果:
- 这种新的分布,称为Poisson-Ramos-Louzada (PRL) 分布,具有可取的统计和可靠性特征.
- 模拟结果显示了PRL模型参数最有效的估计方法.
- 经验应用表明,基于PRL的模型提供了更好的适应性,并优于竞争的单参数离散分布.
结论:
- 拟议的Poisson-Ramos-Louzada分布是离散概率分布家族的一个有价值的补充.
- PRL模型提供了更高的灵活性和性能,使其适合各种统计建模任务.
- 该研究通过理论推导和实践示例验证了新分布的实用性和优越性.
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