Stationary nonseparable space-time covariance functions on networks

• 成果类型: Article
• 署名作者: Porcu, Emilio; White, Philip A.; Genton, Marc G.
• 署名单位: Khalifa University of Science & Technology; Berry Consultants, LLC; Brigham Young University; King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkad082
• 发表日期: 2024
• 页码: 1417-1440
• 关键词: spatial statistical-models; gaussian fields; point patterns; Scoring rules; prediction; accidents; distance; NONSTATIONARY; algorithms; criteria

摘要

The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain is not a planar surface, a sphere, or a linear network, but a generalised network (termed a graph with Euclidean edges). Additionally, data are repeatedly measured over different temporal instants. We provide new classes of stationary nonseparable space-time covariance functions where space can be a generalised network, a Euclidean tree, or a linear network, and where time can be linear or circular (seasonal). Because the construction principles are technical, we focus on illustrations that guide the reader through the construction of statistically interpretable examples. A simulation study demonstrates that the correct model can be recovered when compared to misspecified models. In addition, our simulation studies show that we effectively recover simulation parameters. In our data analysis, we consider a traffic accident dataset that shows improved model performance based on covariance specifications and network-based metrics.