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Journal article
Bayesian Modeling with Spatial Curvature Processes
Journal of the American Statistical Association, v 119(546), pp 1155-1167
02 Apr 2024
PMID: 39006311
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response. We develop Bayesian modeling and inference for rapid changes on the response surface to assess directional curvature along a given trajectory. Such trajectories or curves of rapid change, often referred to as wombling boundaries, occur in geographic space in the form of rivers in a flood plain, roads, mountains or plateaus or other topographic features leading to high gradients on the response surface. We demonstrate fully model based Bayesian inference on directional curvature processes to analyze differential behavior in responses along wombling boundaries. We illustrate our methodology with a number of simulated experiments followed by multiple applications featuring the Boston Housing data; Meuse river data; and temperature data from the Northeastern United States. Supplementary materials for this article are available online.
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Details
- Title
- Bayesian Modeling with Spatial Curvature Processes
- Creators
- Aritra Halder - Drexel University, Epidemiology and BiostatisticsSudipto Banerjee - University of California, Los AngelesDipak K. Dey - University of Connecticut
- Publication Details
- Journal of the American Statistical Association, v 119(546), pp 1155-1167
- Publisher
- Taylor & Francis
- Number of pages
- 13
- Grant note
- Division of Mathematical Sciences R01ES030210; R01ES027027 / National Institute of Environmental Health Sciences (NIEHS) DMS-2113778; DMS-1916349 / National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000945643000001
- Scopus ID
- 2-s2.0-85150504014
- Other Identifier
- 991021861173404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability