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对于过度分散的数据,连续Muth分布的新离散类比:属性,估计技术和应用
Howaida Elsayed1, Mohamed Hussein1,2
1Department of Business Administration, College of Business, King Khalid University, Abha 61421, Saudi Arabia.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
引入了一个新的离散Muth (DsMuth) 分布,用于灵活建模过度分散的计数数据. 这种新的概率质量函数与现有的离散分布相比,提供了改进的分析.
科学领域:
- 统计 统计 统计 统计
- 可能性理论概率理论.
- 可靠性工程可靠性工程
背景情况:
- 计数数据经常显示过度分散,这给标准离散分布带来了挑战.
- 现有的离散概率质量函数可能无法充分捕捉过度分散的数据的复杂性.
研究的目的:
- 引入一个新的单参数离散Muth (DsMuth) 分布.
- 提供灵活的概率质量函数,用于模拟过度分散的计数数据.
- 评估DSMuth分布的属性和估计方法.
主要方法:
- 使用生存分离方法推导DsMuth分布.
- 调查关键的统计属性:平均值,方差,斜率,曲化,PGF,MGF,平均残余寿命,量子函数和.
- 参数估计技术的探索:最大概率,时刻和比例方法.
主要成果:
- 导出DsMuth分布,并分析其基本特征.
- 模拟研究评估不同参数估计器在不同条件下的性能.
- 当应用到真实世界的数据集时,DSMuth分布显示出意义.
结论:
- 拟议的离散Muth (DsMuth) 分布为分析过度分散的计数数据提供了一个有价值的新工具.
- 该研究验证了DSMuth分布的灵活性及其估计方法的有效性.
- DsMuth分布显示了实际的实用性和与现有模型相比的潜在优势.
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