论文标题
空间同质性学习用于空间相关的功能数据与应用于Covid-19的增长率曲线的应用
Spatial homogeneity learning for spatially correlated functional data with application to COVID-19 Growth rate curves
论文作者
论文摘要
我们通过分析美国的COVID-19增长率曲线来研究空间异质性对区域covid-19的大流行时机和严重程度的影响。我们提出了一种在完全捕获大流行曲线中的空间相关性之前，提出了配备有条件回归（CAR）的地理详细功能数据分组方法。然后，可以通过地理位置加权的中国餐厅工艺检测到空间同质性模式，该过程允许本地连续的群体和全球不连续的群体。我们设计有效的马尔可夫链蒙特卡洛（MCMC）算法，以同时推断组数量的后验分布和空间功能数据的分组配置。使用模拟研究以及对美国Covid-19州级和县级数据研究的应用，证明了所提出的方法的优越数值性能，而不是竞争方法。
We study the spatial heterogeneity effect on regional COVID-19 pandemic timing and severity by analyzing the COVID-19 growth rate curves in the United States. We propose a geographically detailed functional data grouping method equipped with a functional conditional autoregressive (CAR) prior to fully capture the spatial correlation in the pandemic curves. The spatial homogeneity pattern can then be detected by a geographically weighted Chinese restaurant process prior which allows both locally spatially contiguous groups and globally discontiguous groups. We design an efficient Markov chain Monte Carlo (MCMC) algorithm to simultaneously infer the posterior distributions of the number of groups and the grouping configuration of spatial functional data. The superior numerical performance of the proposed method over competing methods is demonstrated using simulated studies and an application to COVID-19 state-level and county-level data study in the United States.
