PhD, Statistics, University of Queensland, Australia. 2008
- Optimal experimental design
- Bayesian computational algorithms
- Big data analytics
Selected research outputs (also see Google Scholar and www.jamesmcgree.com)
- Overstall, A. M. and McGree, J. M. (2019) Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation. Bayesian Analysis. Accepted for publication.
- Gedara, J., Drovandi, C. C. and McGree, J. M. (2019) Bayesian sequential design for Copula models. TEST. Accepted for publication.
- Firn, J., McGree, J. M., Harvey, E., Schütz, M., Flores, H., Buckley, Y. M., Lind, E. M., Borer, E. T., Seabloom, E. W., La Pierre, K. J., MacDougall, A. M., Prober, S. M., Stevens, C. J., Sullivan, L., Porter, E., LaDouceur, E., Allen, C., Moromizato, K. H., Eisenhauer, N., Wright, J., Arnillas, C. A., Harpole, W. S. and Risch, A.C. (2019) Leaf nutrient contents, but not specific leaf area, increase rapidly and predictably in response to eutrophication. Nature Ecology and Evolution, 3, 400-406.
- Fisher, R., Shiell, G., Sadler, R., Inostroza, K., Shedrawi, G., Holmes, T. and McGree, J. M. (2019) ‘epower’: an R package for power analysis of Before-After-Control-Impact (BACI) designs. Methods in Ecology and Evolution. Accepted for publication.
- Dehideniya, M., Drovandi, C. C. and McGree, J. M. (2018) Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology. Computational Statistics & Data Analysis, 124, 277-297.
- Overstall, A. M., McGree, J. M. and Drovandi, C. C. (2017) An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing, 28, 343-358.
- Drovandi, C. C., Holmes, C., McGree, J. M., Mengersen, K., Ryan, E. and Richardson, S. (2017) Principles of experimental design for big data analysis. Statistical Science, 32, 385-404.
- McGree, J. M. (2017) Developments of the total entropy utility function for the dual
purpose of model discrimination and parameter estimation in Bayesian design. Computational Statistics & Data Analysis, 113, 207-225.
- Woods, D. C., McGree, J. M. and Lewis, S. M. (2017) Model selection via Bayesian information capacity designs for generalised linear models. Computational Statistics & Data Analysis, 113, 226-238.
- McGree, J. M., Drovandi, C. C., White, G. and Pettitt, A. N. (2016) A pseudo-marginal
sequential Monte Carlo algorithm for random effects models in Bayesian sequential design. Statistics and Computing, 26, 1121-1136.
- Kang, S. Y., McGree, J. M., Drovandi, C.C., Mengersen, K. and Caley, J. (2016)
Bayesian adaptive design: Improving the effectiveness of reef monitoring programs. Ecological Applications, 26, 2637-2648.
- Liu, S., McGree, J. M., Ge, Z. and Xie, Y. (2015). Computational and statistical
methods for analysing big data with applications. Cambridge University Press.
- Ryan, E., Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2016) A review of modern
computational algorithms for Bayesian optimal design. International Statistical Review, 84, 128-154.
- Liu, S., Anh, V., McGree, J. M., Kozan, E. and Wolff, R. C. (2015) A new approach
to the interpolation of complex spatial data. Stochastic Environmental Research and Risk Assessment, 29, 1679-1690.
- Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2014) A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design. Journal of Computational and Graphical Statistics, 23, 3-24.
- Falk, M. G., McGree, J. M. and Pettitt, A. N. (2014) Sampling designs on stream networks using the pseudo-Bayesian approach. Environmental and Ecological Statistics, 21, 751-773.
- Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2013) Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data. Computational Statistics & Data Analysis, 57, 320-335.
- McGree, J. M. and Eccleston, J. A. (2012) Robust designs for Poisson regression models. Technometrics, 54, 64-72.
- McGree, J. M., Drovandi, C. C., Thompson, H. M., Eccleston, J. A., Duffull, S. B., Mengersen, K., Pettitt, A. N. and T. Goggin (2012) Adaptive Bayesian compound designs for dose finding studies. Journal of Statistical Planning and Inference, 142, 1480-1492.
- McGree, J. M., Drovandi, C. C. and Pettitt, A. N. (2012) A sequential Monte Carlo approach to design for population pharmacokinetics studies. Journal of Pharmacokinetics and Pharmacodynamics, 39, 519-526.
- Denman, N. G., McGree, J. M., Eccleston, J. A. and Duffull, S. B. (2011) Design of experiments for bivariate binary responses modelled by Copula functions. Computational Statistics & Data Analysis, 55, 1509-1520.
- McGree, J. M. and Eccleston, J. A. (2008) Probability-based optimal design. Australian and New Zealand Journal of Statistics, 50, 13-28.
- McGree, J.M., Duffull, S.B., Ward, L.C. and Eccleston, J.A. “Impedance Measures”, 25 May 2007, Patent AU2007000726.
Selected PhD projects
- Bayesian design for dependent data
- Model-based adaptive monitoring: Improving the effectiveness of reef monitoring programs
- Bayesian adaptive design for monitoring submerged shoals
- Optimal Bayesian experimental designs for complex models (Completed)
- Detection of longitudinal brain atrophy patterns consistent with progression towards Alzheimer’s Disease (Completed)
- Modelling Parkinson Disease using Bayesian Variable Selection and Association Analysis (Completed)
- Bayesian modelling of breast cancer data (Completed)
- Resilience of South-East Queensland’s water supply (Completed)
- Bayesian models for spatio-temporal assessment of disease (Completed)
- Bayesian survival analysis using gene expression (Completed)
- Bayesian algorithms with applications (Completed)
- Optimal experimental design for nonlinear models with pharmacokinetic-pharmacodynamic applications (Completed)
- Location: GP-O514
- Phone: (+61) 7 3138 2313
- Email: email@example.com