空间相关函数数据的空间均匀性学习及其在COVID-19增长率曲线中的应用
Spatial homogeneity learning for spatially correlated functional data with application to COVID-19 Growth rate curves
摘要：
我们通过分析美国的COVID-19增长率曲线，研究了空间异质性对区域COVID-19大流行时间和严重程度的影响。我们提出了一个地理上详细的功能数据分组方法配备了功能条件自回归（CAR）之前，充分捕捉流行曲线的空间相关性。空间同质性模式，然后可以检测到一个地理加权的中国餐馆的过程之前，允许本地空间连续的组和全球不连续的组。设计了一种有效的马尔可夫链蒙特卡罗（MCMC）算法，用于同时推断空间函数数据的组数和分组结构的后验分布。通过模拟研究和对美国COVID-19州级和县级数据研究的应用，证明了所提出的方法优于竞争方法的数值性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
