在具有离散观察时间的反复事件数据中调查网络结构
Yufeng Xia1, Yangkuo Li1, Xiaobing Zhao2
1School of Data Sciences, Zhejiang University of Finance and Economics, Xueyuan Street, Hangzhou, 310018, Zhejiang Province, China.
Lifetime data analysis
|May 23, 2025
概括
这项研究引入了一种新的统计模型,用于分析纵向网络中反复发生的事件,从而提高我们对随时间推移复杂交互模式的理解.
科学领域:
- 网络科学 网络科学
- 统计建模 统计建模
- 流行病学 流行病学
背景情况:
- 纵向网络对于理解动态系统至关重要.
- 在现实世界的互动中,反复发生的事件过程是很常见的.
- 离散的观察时间带来了独特的分析挑战.
研究的目的:
- 开发一个统计框架,用于分析具有反复事件的纵向网络中的双对相互作用.
- 为了适应随机区块模型用于离散时间观测.
- 为了准确估计相互作用强度函数.
主要方法:
- 使用了随机区块模型框架.
- 应用了变化的EM算法和变化的最大概率估计.
- 使用一种新的方法来估计边缘强度函数,使用分布函数F和自我一致性算法.
主要成果:
- 拟议的估计程序有效地揭示了纵向网络中的底层结构.
- 数字模拟证明了该方法的性能.
- 该模型成功地分析了真实世界的交互数据.
结论:
- 开发的统计方法为纵向网络中的反复事件过程提供了可靠的推断.
- 这种方法增强了动态相互作用数据的分析,特别是在离散观测的情况下.
- 这些发现对网络分析和疾病监测有影响.
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