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一个新的指数分布家族,可用于工程和医学数据的应用

Laxmi Prasad Sapkota¹, Nirajan Bam², Vijay Kumar³

¹Department of Statistics, Tribhuvan University, Tribhuvan Multiple Campus, Palpa, Nepal. laxmisapkota75@gmail.com.

Scientific reports

|September 29, 2025

概括

研究人员开发了一种新的概率分布,即基于指数分布的新奇数式指数分布. 这种新型模型提供灵活的危险功能,并在现实世界数据分析中表现出强的性能.

科学领域:

统计统计统计统计
可能性理论概率理论.
数学建模的数学建模

背景情况:

开发灵活的概率分布对于准确的统计建模至关重要.
指数分布是一个基础模型,但它的局限性需要扩展.
需要新的分布来有效地捕获复杂的数据模式.

研究的目的:

介绍和描述一个新的概率分布:新奇数类型的指数分布.
探索这个新型分布的数学属性和危险函数形状.
用现实数据评估新奇型指数分布的性能和适用性.

主要方法:

从指数分布中推导出新的奇数类型的指数分布.
使用经典 (最大概率) 和贝叶斯方法进行参数估计.
模拟研究,以评估跨样本大小的估计准确性 (偏差,MSE).
应用到现实世界的数据集使用两个估计方法.
通过合适性测试和信息标准进行模型性能评估.
使用哈密尔顿蒙特卡洛和后期预测检查的贝叶斯分析.

主要成果:

新奇型指数分布表现出多种不同的危险函数形状 (反向J,下降,恒定,浴,S形).
经典估计方法显示,随着样本大小的增加,偏差和平均平方误差减少.
拟议的分发系统在现实数据上始终优于现有模型.
贝叶斯分析证实了模型的稳定性和预测准确性.

结论:

新奇型指数分布是统计建模的一个数学上可处理和多功能补充.
经典和贝叶斯估计方法都对这种新分布有效.
该模型展示了实用的实用性和优越的性能,与现实世界的应用程序的既定发行版相比.

关键词:

贝叶斯的方法是贝叶斯的方法.指数分布的分布是指数的分布.这就是G-家族.汉密尔顿式蒙特卡洛的蒙特卡洛的随后的分发后续的分发

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