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)
- 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. Accepted for publication – March, 2018.
- 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.
- Innovating optimal experimental design through Bayesian statistics
- Adaptive monitoring of large scale ecological systems
- Sequential Bayesian design using the Integrated Nested Laplace approximation.
- Detection of longitudinal brain atrophy patterns consistent with progression towards Alzheimer’s Disease.
- Modelling Parkinson Disease using Bayesian Variable Selection and Association Analysis.
- 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: firstname.lastname@example.org