使用统一的灵活家族建模生命周期和计数数据:其离散对应物,属性和推断
Ahmed Z Afify1, Maha M Helmi2, Hassan M Aljohani2
1Department of Statistics, Mathematics, and Insurance, Benha University, Benha 13511, Egypt.
PloS one
|April 17, 2025
概括
本研究介绍了灵活修改的Kavya-Manoharan-G (MKM-G) 和离散的MKM-G (DMKM-G) 分布,用于模拟各种现实数据. 这些新模型有效地捕捉复杂的危险率,并适合环境,医学和工程数据集.
科学领域:
- 统计建模 统计建模
- 可能性分布的概率分布.
背景情况:
- 现有的统计模型对于复杂的现实数据可能缺乏灵活性.
- 建模生命周期和计数数据通常需要能够实现非单调危险率的分布.
研究的目的:
- 引入两个新的灵活分发家族:修改的Kavya-Manoharan-G (MKM-G) 和离散的MKM-G (DMKM-G).
- 研究这些新家族的属性和估计方法.
- 使用现实世界的数据,证明拟议模型的适用性和优越性.
主要方法:
- MKM-G 和 DMKM-G 分布家族的发展.
- 理论分析MKM-G家族的基本特性.
- 对MKM指数 (MKME) 参数应用八种经典估计方法.
- 模拟研究用于评估估计器性能.
- 定义和分析离散的MKM指数分布 (DMKME).
主要成果:
- 拟议的MKM-G和DMKM-G家族提供了更大的灵活性,特别是由于额外的形状参数.
- 子模型可以有效地表示单调和非单调的危险率.
- 模拟结果表明MKME参数的不同估计方法的性能.
- 实际数据分析证实,MKME和DMKME分布与现有模型相比,提供了非常接近的匹配.
结论:
- MKM-G和DMKM-G家族是统计建模的宝贵补充,提供更大的灵活性.
- 这些分布适合在各种科学领域建模各种寿命和计数数据.
- 拟议的模型表现出强大的性能和适应现实应用的适应性.
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