PhD (Statistics), QUT.
B AppSc (Hons: Statistics), QUT.
B Math (Statistics), QUT.
B Bus (Accounting, International Business), QUT.
- Spatial modelling (e.g. disease mapping)
- Spatio-temporal modelling
- Data visualisation (especially spatial visualisations, e.g. choropleth and other thematic maps, cartograms)
- Bayesian mixture models and development of relabelling algorithms to reverse label switching
- Environmental statistics and sustainability
My current project is the development of an Australian national cancer atlas. The end result will be a publicly accessible , online, interactive map (think Google Earth) showing estimated standardised incidence ratio and survival rates for many different types of cancers. I work with Cancer Council Queensland and the QUT Visualisation and eResearch (ViseR) team to combine our expertise in statistical spatial modelling, data visualisation, and UX and UI design. My main contribution is the development of a new spatial model designed to avoid the issues of over- and under-smoothing, compare this model against other published methods using simulated data, and apply the model to real incidence and survival data which will be displayed in the cancer atlas. Other contributions include finding the best (or at least good) ways to represent uncertainty around estimates.
Duncan, E. W. 2017. Bayesian approaches to issues arising in spatial modelling. PhD Thesis, Queensland University of Technology. URL: https://eprints.qut.edu.au/112356/1/Earl_Duncan_Thesis.pdf.
- Cramb S. M., E. W. Duncan, P. D. Baade, and K. L. Mengersen. 2018. Investigation of Bayesian spatial models. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT). URL: https://eprints.qut.edu.au/115590.
- Duncan, E. W., N. M. White, and K. Mengersen. 2017. Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference. International Journal of Health Geographics 16 (1): 47. DOI: 10.1186/s12942-017-0120-x.
- Cramb, S. M., E. W. Duncan, N. M. White, P. D. Baade, and K. L. Mengersen. 2016. Spatial Modelling Methods. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT).
- Duncan, E. W., N. M. White, and K. Mengersen. 2016. Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation. BMJ Open 6 (5): p.e010253. DOI: 10.1136/bmjopen-2015-010253.
- Pokorny, M. R., M. de Rooij, E. Duncan, F. H. Schröder, R. Parkinson, J. O. Barentsz, and L. C. Thompson. 2014. Prospective Study of Diagnostic Accuracy Comparing Prostate Cancer Detection by Transrectal Ultrasound–Guided Biopsy Versus Magnetic Resonance (MR) Imaging with Subsequent MR-guided Biopsy in Men Without Previous Prostate Biopsies. European Urology 66 (1): 22-29. DOI: 10.1016/j.eururo.2014.03.002.
- Mengersen, K., E. W. Duncan, N. M. White, J. Arbel, and C. Alston-Knox. “Applications in Industry”. In Handbook of Mixture Analysis, edited by G. Celeux, S. Früwirth-Schnatter, and C. P. Robert. Chapman & Hall/CRC Handbooks of Modern Statistical Methods. Forthcoming July 2018.
Duncan, E. W., N. M. White, and K. Mengersen. Improved Bayesian methods for identifying aberrant temporal trends in spatio-temporal data with application to mammography screening services. Submitted to PLOS ONE, May 2017.
- 18 Aug 2016: Deriving the Full Conditionals
- 20 July 2017: Reversing Label Switching
- 29 March 2018: Cartograms