| Autor: |
Franco-Villoria M; Department of Economics, 9306University of Modena and Reggio Emilia, Italy., Ventrucci M; Department of Statistical Sciences, University of Bologna, Italy., Rue H; CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. |
| Jazyk: |
angličtina |
| Zdroj: |
Statistical methods in medical research [Stat Methods Med Res] 2022 Aug; Vol. 31 (8), pp. 1566-1578. Date of Electronic Publication: 2022 May 18. |
| DOI: |
10.1177/09622802221099642 |
| Abstrakt: |
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 model. The variance partitioning 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 an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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