Lee Junho, Kamenetsky Maria E, Gangnon Ronald E, Zhu Jun
Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Stat Med. 2021 Jan 30;40(2):465-480. doi: 10.1002/sim.8785. Epub 2020 Oct 25.
In regression analysis for spatio-temporal data, identifying clusters of spatial units over time in a regression coefficient could provide insight into the unique relationship between a response and covariates in certain subdomains of space and time windows relative to the background in other parts of the spatial domain and the time period of interest. In this article, we propose a varying coefficient regression method for spatial data repeatedly sampled over time, with heterogeneity in regression coefficients across both space and over time. In particular, we extend a varying coefficient regression model for spatial-only data to spatio-temporal data with flexible temporal patterns. We consider the detection of a potential cylindrical cluster of regression coefficients based on testing whether the regression coefficient is the same or not over the entire spatial domain for each time point. For multiple clusters, we develop a sequential identification approach. We assess the power and identification of known clusters via a simulation study. Our proposed methodology is illustrated by the analysis of a cancer mortality dataset in the Southeast of the U.S.
在时空数据的回归分析中，在回归系数中识别随时间变化的空间单元聚类，可以洞察响应变量与协变量在时空窗口的某些子域中相对于空间域其他部分的背景以及感兴趣的时间段内的独特关系。在本文中，我们提出了一种针对随时间重复采样的空间数据的变系数回归方法，其回归系数在空间和时间上均具有异质性。特别是，我们将仅空间数据的变系数回归模型扩展到具有灵活时间模式的时空数据。我们考虑基于检验每个时间点的回归系数在整个空间域上是否相同来检测回归系数的潜在圆柱形聚类。对于多个聚类，我们开发了一种顺序识别方法。我们通过模拟研究评估已知聚类的功效和识别情况。我们提出的方法通过对美国东南部癌症死亡率数据集的分析进行了说明。
