Journal article
Bayesian Spatiotemporal Wombling
Journal of the American Statistical Association, pp 1-28
14 Jul 2026
Abstract
Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the values of the random field (or spatial surface indexed continuously over time) at arbitrary space-time coordinates, scientific interest often turns to gleaning information regarding zones of rapid spatial-temporal change. We develop Bayesian modeling and inference for directional rates of change along a given surface. These surfaces, which demarcate regions of rapid change, are referred to as “wombling” surface boundaries. Existing methods for studying such changes have often been associated with curves and are not easily extendable to surfaces resulting from curves evolving over time. Our current contribution devises a fully model-based inferential framework for analyzing differential behavior in spatiotemporal responses by formalizing the notion of a “wombling” surface boundary using conventional multi-linear vector analytic frameworks and geometry followed by posterior predictive computations using triangulated surface approximations. We illustrate our methodology with comprehensive simulation experiments followed by multiple applications in neuroimaging; environmental and climate science and pollutant analysis in environmental health.
Metrics
3 Record Views
Details
- Title
- Bayesian Spatiotemporal Wombling
- Creators
- Aritra Halder - Drexel UniversityDidong Li - University of North Carolina at Chapel HillSudipto Banerjee (Corresponding Author) - University of California, Los Angeles
- Publication Details
- Journal of the American Statistical Association, pp 1-28
- Publisher
- Taylor & Francis
- Number of pages
- 28
- Grant note
- National Institute of Environmental Health Sciences Division of Mathematical Sciences National Institute of General Medical Sciences
AH acknowledges funding from the National Science Foundation, Division of Mathematical Sciences (DMS 2515897), the National Institutes of Health, National Institute of Environmental Health Sciences (R56 ES036250, R21 CA305033), and institutional funds at the Dornsife School of Public Health and the Urban Health Collaborative at Drexel University. DL acknowledges funding from the National Science Foundation, Division of Mathematical Sciences (DMS 2515899), and the National Institutes of Health (P42 ES031007, P50 CA058223, P30 ES010126, R01 LM014407). SB acknowledges funding from the National Science Foundation, Division of Mathematical Sciences (DMS 2113778 and 2515898), and the National Institute of Health, National Institute of General Medical Sciences (R01 GM148761).
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Epidemiology and Biostatistics
- Web of Science ID
- WOS:001870851000001
- Other Identifier
- 991022197151604721