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Variance partitioning in spatio-temporal disease mapping models
- Publication Year :
- 2021
-
Abstract
- Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov Random Fields, that we name the variance partitioning (VP) model. The VP model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding prior information in a intuitive way. We illustrate the advantages of the VP model using two case studies.
- Subjects :
- Statistics - Methodology
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2109.13374
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1177/09622802221099642