Stationary nonseparable space-time covariance functions on networks

Emilio Porcu, Philip A. White, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Bibliographical note

KAUST Repository Item: Exported on 2023-09-18
Acknowledgements: We acknowledge Jun Tang for sharing code and data, and we thank the review team for comments that improved the paper. This work was supported by the Khalifa University of Science and Technology Award No. FSU-2021-016 (E. Porcu), NSF-DMS CDS&E grant 2053188 (P. White), and the King Abdullah University of Science and Technology (M. Genton).

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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