Our research encompasses a range of topics in the design and analysis of experiments, including theory, methodology and computation.
Much of our work is motivated by important substantive applications from areas of science, engineering and industry.
|Design for nonlinear models
- Design theory and methods for general classes of nonlinear models
- Designs for generalised linear models
- Designs for survival models with censored data
- Atkinson, A.C. and Woods, D.C. (2015). Designs for generalized linear models. In "Handbook of Design and Analysis of Experiments", editors: Dean, A.M., Morris, M.D., Stufken, J. and Bingham, D.R.
Chapman & Hall/CRC Press, Boca Raton (Publisher website; arXiv:1510.05253).
- Konstaninou, M., Biedermann, S. and Kimber, A. (2014). Optimal designs for two-parameter nonlinear models with application to survival models. Statistica Sinica, 24, 415-428
- Yang, M., Biedermann, S. and Tang, E. (2013). On optimal designs for nonlinear models: a general and efficient algorithm. Journal of the American Statistical Association, 108, 1411-1420
- Biedermann, S., Dette, H. and Woods, D.C. (2011). Optimal design for additive partially nonlinear models. Biometrika, 98, 449-458
- Biedermann, S. and Woods, D.C. (2011). Optimal designs for generalised nonlinear models with application to second harmonic generation experiments. Journal of the Royal Statistical Society, Series C, 60, 281-299
- Woods, D.C., Lewis, S.M., Eccleston, J.A. and Russell, K.G. (2006). Designs for generalized linear models with several variables and model uncertainty. Technometrics, 48, 284-292
- Variable selection for supersaturated designs
- Dimension-reduction in computer experiments
- Bayesian model selection for screening
- Overstall, A.M. and Woods, D.C. (2016). Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model.
Journal of the Royal Statistical Society Series C, 65, 483-505 (doi:10.1111/rssc.12141)
- Woods, D.C. and Lewis, S.M. (2016). Design of experiments for screening. In "Handbook of Uncertainty Quantification",
editors: Ghanem, R., Hidgon, D. and Owhadi, H. Springer, New York, in press (arXiv:1510.05248)
- Draguljic, D., Woods, D.C., Dean, A.M., Lewis, S.M. and Vine, A.E. (2014). Screening strategies in the presence of interactions (with discussion).
Technometrics, 56, 1-28 (doi:10.1080/00401706.2013.775900)
- Marley, C.J. and Woods, D.C. (2010). A comparison of design and model selection methods for supersaturated designs. Computational Statistics and Data Analysis, 54, 3158-3167
|Computer experiments and uncertainty quantification
- Emulation of high-dimensional computer models
- Bayesian computation for computationally expensive physical models
- Space-filling designs
- Bowman, V.E. and Woods, D.C. (2016). Emulation of multivariate simulators using thin-plate splines with application to atmospheric dispersion. SIAM/ASA Journal of Uncertainty Quantification, 4, 1323-1344
- Overstall, A.M. and Woods, D.C. (2016). Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model. Journal of the Royal Statistical Society Series C, 65, 483-505
- Woods, D.C. and Lewis, S.M. (2016). Design of experiments for screening. In "Handbook of Uncertainty Quantification", editors: Ghanem, R., Hidgon, D. and Owhadi, H. Springer, New York, in press
- Bowman, V.E. and Woods, D.C. (2013). Weighted space-filling designs. Journal of Simulation, 7, 249-263 (doi:10.1057/jos.2013.8).
- Overstall, A.M. and Woods, D.C. (2013). A strategy for Bayesian inference for computationally expensive models with application to the estimation of stem cell properties. Biometrics, 69, 458-468
- Approximation of expected utilities for design assessment
- Computational algorithms for design selection
- Applications in science and technology
- Overstall, A.M. and Woods, D.C. (2016). Bayesian design of experiments using approximate coordinate exchange. Technometrics, in press
- Woods, D.C., Overstall, A.M., Adamou, M. and Waite, T.W. (2016). Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application (with discussion). Quality Engineering, 29, 91-118
- Overstall, A.M., McGree, J.M. and Drovandi, C.C. (2016). Fully Bayesian optimal design using the approximate coordinate exchange algorithm and normal-based approximations to posterior quantities. arXiv:1608.05815
|Design for dependent data
- Design for nonlinear and generalised linear mixed models
- Design for networks
- Parker, B.M., Gilmour, S.G. and Schormans, J. (2016). Optimal design of experiments on connected units with application to social networks. Journal of the Royal Statistical Society Series C, in press
- Waite, T.W. and Woods, D.C. (2015). Designs for generalized linear models with random block effects via information matrix approximations. Biometrika, 102, 677-693
- Woods, D.C. and van de Ven, P. (2011). Blocked designs for experiments with non-normal response. Technometrics, 53, 173-182