Working papers

  • Phoa, F. K. H., Chou, S.-K. and Woods, D. C. (2017). Summary of effect aliasing structure (SEAS) - new descriptive statistics for factorial and supersaturated designs. Submitted (https://arxiv.org/abs/1711.11488).

Publications

  • Rappold, A., Müller, W. G. and Woods, D. C. (2019). Copula-based robust optimal block designs. Applied Stochastic Models in Business and Industry, in press (https://arxiv.org/abs/1811.02414).
  • Overstall, A. M., Woods, D. C. and Parker, B. M. (2019). Bayesian optimal design for ordinary differential equation models with application in biological science. Journal of the American Statistical Association, in press (http://arxiv.org/abs/1509.04099).
  • Overstall, A. M., Woods, D. C. and Adamou, M. (2019). acebayes - an R package for Bayesian optimal design of experiments via approximate coordinate exchange. Journal of Statistical Software, in press (https://arxiv.org/abs/1705.08096).
  • Ramkumar, P., Harvey, T. J., Wood, R. J. K., Rose, A. D., Woods, D. C. and Lewis, S. M. (2018). Factorial study of diesel oil contamination effects on steel and ceramic sliding contacts. Journal of Engineering Tribology, in press (doi:10.1177/1350650118794730).
  • Overstall, A. M., Woods, D. C. and Martin, K. (2018). Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics. Computational Statistics and Data Analysis, in press (doi:10.1016/j.csda.2018.10.013).
  • Woods, D. C., Overstall, A. M., Adamou, M. and Waite, T. W. (2017). Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application (with discussion). Quality Engineering, 29 91-118 (doi:10.1080/08982112.2016.1246045).
  • Woods, D. C., McGree, J. M. and Lewis, S. M. (2017). Model selection via Bayesian information capacity designs for generalised linear models. Computational Statistics and Data Analysis, 113 226-238 (doi:10.1016/j.csda.2016.10.025).
  • Woods D. C. and Lewis S. M. (2017). Design of experiments for screening. Handbook of Uncertainty Quantification, editors: Ghanem, R., Hidgon, D. and Owhadi, H. Springer, New York (http://arxiv.org/abs/1510.05248).
  • Overstall A. M. and Woods D. C. (2017). Bayesian design of experiments via approximate coordinate exchange. Technometrics, 59 458-470 (doi:10.1080/00401706.2016.1251495).
  • 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 C, 65 485-505 (doi:10.1111/rssc.12141).
  • 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 (doi:10.1137/140970148).
  • 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 (doi:10.1093/biomet/asv005).
  • Lendrem, D. W., Lendrem, B. C., Woods, D. C., Rowland-Jones, R., Burke, M., Chatfield, M., Isaacs, J. D. and Owen, M. R. (2015). Lost in space - design of experiments and scientific exploration in a Hogarth universe. Drug Discovery Today, 20 1365-1371 (doi:10.1016/j.drudis.2015.09.015).
  • Atkinson A. C. and Woods D. C. (2015). Designs for generalized linear models. Handbook of Design and Analysis of Experiments, editors: Dean, A. M., Morris, M. D., Stufken, J. and Bingham, D. R. Chapman & Hall/CRC, Boca Raton (http://arxiv.org/abs/1510.05253).
  • van de Ven P. and Woods D. C. (2014). Optimal blocked minimum-support designs for non-linear models. Journal of Statistical Planning and Inference, 144 152-159 (doi:10.1016/j.jspi.2013.02.001).
  • Draguljić, 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).

    Awarded 2015 Youden Prize for best expository paper in the 2014 volume of Technometrics.

  • 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 (doi:10.1111/biom.12017).
  • Fisher, V. A., Woods, D. C. and Lewis, S. M. (2013). Optimal design for prediction using local linear regression and the D_SI-criterion. Statistics and Applications, 11 33-54 (http://www.ssca.org.in/media/Paper3.pdf).
  • Bowman V. E. and Woods D. C. (2013). Weighted space-filling designs. Journal of Simulation, 7 249-263 (doi:10.1057/jos.2013.8).
  • Woods D. C. and van de Ven P. (2011). Blocked designs for experiments with non-normal response. Technometrics, 53 173-182 (doi:10.1198/TECH.2011.09197).
  • Woods D. C. and Lewis S. M. (2011). Continuous optimal designs for generalized linear models under model uncertainty. Journal of Statistical Theory and Practice, 5 137-145 (doi:10.1080/15598608.2011.10412056).
  • 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 C, 60 281-299 (doi:10.1111/j.1467-9876.2010.00749.x).
  • Biedermann, S., Dette, H. and Woods, D. C. (2011). Optimal designs for additive partially nonlinear models. Biometrika, 98 449-458 (doi:10.1093/biomet/asr001).
  • Woods, D. C. (2010). Robust designs for binary data - applications of simulated annealing. Journal of Statistical Computation and Simulation, 80 29-41 (doi:10.1080/00949650802445367).
  • 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 (doi:10.1016/j.csda.2010.02.017).
  • Russell, K. G., Woods, D. C., Lewis, S. M. and Eccleston, J. A. (2009). D-optimal designs for Poisson regression models. Statistica Sinica, 19 721-730 (http://www3.stat.sinica.edu.tw/statistica/J19N2/j19n217/j19n217.html).
  • Russell, K. G., Eccleston, J. A., Lewis, S. M. and Woods, D. C. (2009). Design considerations for small experiments and simple logistic regresion. Journal of Statistical Computation and Simulation, 79 81-91 (doi:10.1080/00949650701609006).
  • Waterhouse, T. H., Woods, D. C., Eccleston, J. A. and Lewis, S. M. (2008). Design selection criteria for discrimination/estimation for nested models and a binomial response. Journal of Statistical Planning and Inference, 138 132-144 (doi:10.1016/j.jspi.2007.05.017).
  • Woods, D. C., Lewis, S.M., Eccleston, J. A. and Russell, K. G. (2006). Designs for generalised linear models with several variables and model uncertainty. Technometrics, 48 284-292 (doi:10.1198/004017005000000571).
  • Woods D. C. and Lewis S. M. (2006). All-bias designs for polynomial spline regression models. Australian and New Zealand Journal of Statistics, 48 49-58 (doi:10.1111/j.1467-842X.2006.00424.x).
  • Woods, D. C., Grove, D. G., Liccardi, I., Lewis, S. M. and Frey, J. G. (2006). An eLearning website for the design and analysis of experiments with application to chemical processes. Proceedings of Compstat 2006, 1641-1649
  • Woods, D. C. (2005). Designing experiments under random contamination with application to polynomial spline regression. Statistica Sinica, 15 619-635 (http://www3.stat.sinica.edu.tw/statistica/J15N3/J15N32/J15N32.html).
  • Grove, D. G., Woods, D. C. and Lewis, S. M. (2004). Multifactor B-spline mixed models in designed experiments for the engine mapping problem. Journal of Quality Technology, 36 380-391 (http://www.asq.org/qic/display-item/index.html?item=19629).
  • Woods, D. C., Lewis, S. M. and Dewynne, J. N. (2003). Designing experiments for multi-variable B-spline models. Sankhya, 65 660-677