Bayesian approaches to issues arising in spatial modelling
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Earl Duncan Thesis (PDF 6MB) |
Description
This thesis addressed several contemporary issues arising in the analysis of spatial data and the broader statistical methodology. Two state-of-the-art statistical models are developed for the purpose of identifying unusual trends, a new algorithm to deal with label switching is devised which outperforms existing solutions, and new approaches to spatial smoothing are explored. The outcomes from this thesis should be of interest to managers in the health sector, biostatisticians, and researchers who deal with spatial data.
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| ID Code: | 112356 |
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| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | Mengersen, Kerrie & White, Nicole |
| Keywords: | Spatial, Spatio-temporal, Bayesian, Mixture model, Label switching, Cancer, Mammography screening, Epidemiology, Smoothing, Covariate space |
| DOI: | 10.5204/thesis.eprints.112356 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 02 Nov 2017 05:29 |
| Last Modified: | 02 Nov 2017 05:29 |
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