使用通用拉索的时空空间聚类分析与应用程序来揭示日本Covid-19病例的传播
Septian Rahardiantoro1,2, Wataru Sakamoto1
1Department of Human Ecology, Graduate School of Environmental and Life Science, Okayama University, Okayama, 700-8350 Japan.
Computational statistics
|June 26, 2023
概括
这项研究引入了一种新的广义拉索方法,用于时空聚类,有效地识别多个聚类. 该方法在估计COVID-19数据的时间和空间影响方面表现出卓越的表现.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 时空聚类对于理解各种领域 (包括流行病学) 的动态模式至关重要.
- 现有的规范化方法可能无法充分捕捉多个集群或复杂的时空依赖关系.
研究的目的:
- 为探测多个时空集群提出一种全新的通用拉索模型.
- 通过模拟研究,对拟议方法的性能与现有方法进行评估.
- 将该方法应用于现实世界的COVID-19数据,用于动态集群分析.
主要方法:
- 开发了一种具有两种惩罚的通用拉索模型,可分为趋势过 (时间) 和融合拉索 (空间) 组件.
- 用于调整参数选择的员工近似休假一次失败交叉验证 (ALOCV) 和通用交叉验证 (GCV).
- 进行模拟研究,以比较拟议的方法与未处罚,脊,拉索和通用脊方法.
主要成果:
- 与其他方法相比,建议使用ALOCV/GCV的通用拉索方法实现了较低的时间和空间效应估计平均平方误差 (MSE).
- 它在不同真实风险价值结构的时间效应检测中显示出更稳定的和更小的MSE.
- 使用通用拉索的ALOCV在检测空间效应方面获得了更高的准确性,这表明了空间聚类的共同调参数.
结论:
- 拟议的通用拉索框架为多个时空集群检测提供了一种灵活和有效的方法.
- 该方法准确地捕捉了动态集群行为,其应用在日本的COVID-19监测数据中证明了这一点.
- ALOCV和GCV适用于调参数选择,提高时空聚类结果的可靠性.
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