A/Prof. James McGree

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Qualification

PhD, Statistics, University of Queensland, Australia. 2008

Research Interests

  • Optimal experimental design
  • Bayesian computational algorithms
  • Big data analytics

Selected research outputs (also see Google Scholar)

  • Liu, S., McGree, J. M., Ge, Z. and Xie, Y. (2015). Computational and statistical
    methods for analysing big data with applications. Cambridge University Press.
  • 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.
  • 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. Accepted for publication.
  • 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. Accepted for publication.
  • 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. Accepted for publication.
  • Ryan, E., Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2015) 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 projects

  • 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).

Contact details

  • Location: GP-O514
  • Phone: (+61) 7 3138 2313
  • Email: james.mcgree@qut.edu.au
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