Journal article
Space-time covariance models on networks
Electronic journal of statistics, Vol.18(1), pp.490-514
01/01/2024
DOI: 10.1214/23-EJS2206
Abstract
The second -order, small-scale dependence structure of a stochastic process defined in the space-time domain is key to prediction (or kriging). While great efforts have been dedicated to developing models for cases in which the spatial domain is either a finite -dimensional Euclidean space or a sphere, counterpart developments on a generalized linear network are practically non-existent. To fill this gap, we develop a broad range of parametric, non -separable space-time covariance models on generalized linear networks. For the important subgroup of Euclidean trees, we develop models by the space embedding technique, in concert with the generalized Gneiting class of models and 1 -symmetric characteristic functions, and by the convex cone and scale mixture approaches. We give examples from each class of models and investigate the geometric features of these covariance functions near the origin and at infinity. We also reveal connections between different classes of space-time covariance models on Euclidean trees. We conclude the paper by investigating the performance of maximum likelihood estimators of certain proposed models in a simulation study.
Details
- Title: Subtitle
- Space-time covariance models on networks
- Creators
- Jun Tang - University of IowaDale Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Electronic journal of statistics, Vol.18(1), pp.490-514
- Publisher
- INST MATHEMATICAL STATISTICS-IMS
- DOI
- 10.1214/23-EJS2206
- ISSN
- 1935-7524
- eISSN
- 1935-7524
- Number of pages
- 25
- Language
- English
- Date published
- 01/01/2024
- Academic Unit
- Biostatistics; Statistics and Actuarial Science
- Record Identifier
- 9984567871702771
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