一个林德利-二项式模型,用于分析与稀疏度和过度零的比例
Dianliang Deng1, Xiaoqing Zhang1
1Department of Mathematics and Statistics, University of Regina, Sask, Canada.
Journal of applied statistics
|June 27, 2024
概括
一个新的林德利二项式 (LB) 分布有效地分析带有额外变化的比例数据,如零通货膨胀. 这种统计模型为各种科学领域中常见的复杂数据集提供了改进的分析.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 比例数据在科学学科中普遍存在.
- 通常情况下,这些数据表现为多/少分散,稀疏和零通货膨胀,挑战现有的统计模型.
- 例如,肝炎,白和导管管理数据集,所有这些都显示出显著的零通货膨胀和稀少性.
研究的目的:
- 介绍一种新的两参数概率分布,即林德利二项式 (LB) 分布.
- 为分析具有复杂特征的比例数据提供强大的统计工具,例如额外变化和零通货膨胀.
- 解决现有模型在处理这些数据特征方面的局限性.
主要方法:
- 对林德利二项式分布的概率性质的推导,包括时刻和产生时刻的函数.
- 在LB回归模型中开发费舍尔评分和预期最大化 (EM) 算法用于参数估计.
- 讨论拟议LB模型的合适性标准.
- 进行模拟研究,以评估带有和没有共变量的EM算法性能.
主要成果:
- 提出的林德利双项分布证明适合分析零通货膨胀和过度分散的比例数据.
- 费舍尔评分和EM算法为LB回归模型中的参数估计提供了有效的方法.
- 模拟结果表明EM算法对参数估计的可靠性能.
结论:
- 林德利双项分布为建模复杂的比例数据提供了一种有价值的新方法.
- 开发的估计算法促进了LB模型在各种研究领域的实际应用.
- 该模型的实用性通过其应用于具有挑战性数据特征的真实世界数据集而得到证实.
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