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基于I型重尾雷利分布的截断寿命测试的组验收采样计划
Mmesoma P Nwankwo1, Najwan Alsadat2, Anoop Kumar3
1Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, P.O. Box 5025, Awka, Nigeria.
Heliyon
|October 10, 2024
概括
I型重尾雷利分布 (TI-HTR) 为分析数据提供了一个强大的统计模型,对于COVID-19和癌症数据集特别有效. 与传统方法相比,这种重尾分布提供了更好的模型拟合和推理.
科学领域:
- 统计 统计 统计 统计
- 可能性分布的概率分布.
- 统计建模 统计建模
背景情况:
- I型重尾 (TI-HT) 分布家族是统计研究的一个显著领域.
- I型重尾雷利分布 (TI-HTR) 是该家族的一个特定成员,需要详细描述.
研究的目的:
- 为了彻底调查TI-HTR分布的统计性质.
- 开发和评估参数估计技术,包括最大概率和处罚概率估计.
- 评估TI-HTR分布在现实应用中的实用性,并与现有模型进行比较.
主要方法:
- 导出关键的统计属性:时刻,量子函数和可靠性指标.
- 应用最大概率估计 (MLE) 和惩罚性概率估计 (PLE) 进行参数估计.
- 分布函数的图形分析和模型行为的分析调查.
- 基于TI-HTR分布的组验收抽样计划 (GASP) 的设计和评估.
- 模拟现实生活中的COVID-19和癌症数据.
主要成果:
- TI-HTR分布在原点附近呈现线性增长和快速指数衰变,尾巴的行为与传统的权力规律重尾巴不同.
- 与竞争模型相比,TI-HTR分布提供了与COVID-19和癌症数据的优越匹配,从而改善了推断.
- 受到惩罚的概率估计显示出优异的性能,参数估计的标准误差最小.
- 克拉梅尔--米塞斯测试表明TI-HTR分布适用于快速衰变的指数数据,对重尾的敏感性较小.
结论:
- TI-HTR分布是一种有价值的统计工具,特别是用于建模具有快速衰变指数特征的数据集,例如COVID-19和癌症数据.
- 对于参数估计,建议使用惩罚性概率估计,因为其准确度提高,标准误差减少.
- 在特定数据类型的传统模型中,TI-HTR分布在模型匹配和推理能力方面具有优势.
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