论文标题
功能数据分析：美国COVID-19数据的应用
Functional data analysis: An application to COVID-19 data in the United States
论文作者
论文摘要
到目前为止，COVID-19-19对世界各地的不同地区造成了巨大的负面影响，美国（美国）是受影响最大的国家之一。在本文中，我们使用功能数据分析中的方法来研究美国的COVID-19数据。我们通过功能主成分分析（FPCA）探索数据变化的模式，并研究确认和死亡病例之间的规范相关性。此外，我们在州级进行集群分析，以研究地理位置与聚类结构之间的关系。最后，我们考虑了一个功能性时间序列模型，该模型拟合到美国确认的案例，并根据动态FPCA进行预测。提供了点和间隔预测，还包括评估预测准确性的方法。
The COVID-19 pandemic so far has caused huge negative impacts on different areas all over the world, and the United States (US) is one of the most affected countries. In this paper, we use methods from the functional data analysis to look into the COVID-19 data in the US. We explore the modes of variation of the data through a functional principal component analysis (FPCA), and study the canonical correlation between confirmed and death cases. In addition, we run a cluster analysis at the state level so as to investigate the relation between geographical locations and the clustering structure. Lastly, we consider a functional time series model fitted to the cumulative confirmed cases in the US, and make forecasts based on the dynamic FPCA. Both point and interval forecasts are provided, and the methods for assessing the accuracy of the forecasts are also included.
