Identifying boundaries in spatially continuous risk surfaces from spatially aggregated disease count data

DOI10.1214/23-aoas1755OpenAlexW4388089244MaRDI QIDQ6138628

Duncan Lee

Publication date: 16 January 2024

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/23-aoas1755

zbMATH Keywords

Bayesian inferencedisease-risk modellingedge-detection algorithmsspatially continuous inference

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

• Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method
• Bayesian image restoration, with two applications in spatial statistics (with discussion)
• Improved inference for areal unit count data using graph-based optimisation
• Detecting and modeling changes in a time series of proportions
• Boundary detection in disease mapping studies
• Bayesian Wombling
• Hierarchical and Joint Site-Edge Methods for Medicare Hospice Service Region Boundary Analysis
• Hierarchical Spatio-Temporal Mapping of Disease Rates
• Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error
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