Dave Woods

Dave Woods

Professor of Statistics at the University of Southampton

Head of Statistics in the School of Mathematical Sciences.

Working papers

  • Zhang, J., Semochkina, D., Sugisawa, N., Woods, D. C. and Lapkin, A. A. (2024). Multi-objective reaction optimization under uncertainties using expected quantile improvement. ChemRxiv (doi:10.26434/chemrxiv-2024-c1xqv).
  • Chitre, A., Woods, D. C. and Lapkin, A. A. (2024). Machine learning-guided space-filling designs for high throughput liquid formulations development. ChemRxiv (doi:10.26434/chemrxiv-2024-18rgd).

Publications

  • Semochkina, D., Forrester, A. I. J. and Woods, D. C. (2024). Multi-objective optimisation using expected quantile improvement for decision making in disease outbreaks. SIAM/ASA Journal of Uncertainty Quantification, in press (https://arxiv.org/abs/2401.12031).
  • Liao, Y.H., Phoa, F. K. H. and Woods, D. C. (2024). The summary of effect aliasing structure for two-level supersaturated and factorial designs. Journal of Statistical Theory and Practice, 18 53 (doi:10.1007/s42519-024-00407-1). (https://arxiv.org/abs/1711.11488).
  • Tackney, M.S. Woods, D.C. and Shpitser, I. (2023). Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates. Journal of Statistical Simulation and Computation, 93 581-603 (doi:10.1080/00949655.2022.2113788).
  • Cao, L., Russo, D., Matthews, E. S., Lapkin, A. and Woods D. C. (2023). Computer-aided design of formulated products, A bridge design of experiments for ingredient selection. Computers in Chemical Engineering, 169 108083 (doi:10.1016/j.compchemeng.2022.108083).
  • Silk, D.S., Bowman, V.E., Semochkina, D., Dalrymple, U. And Woods, D.C. (2022). Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions. Statistical Methods in Medical Research, 31 1778-1789 (doi:10.1177%2F09622802221109523). (https://arxiv.org/abs/2006.10714).
  • Maishman, T., Schaap, S., Silk, D.S., Nevitt, S.J., Woods, D.C. and Bowman, V.E. (2022). Statistical methods used to combine the effective reproduction number, R(t), and other related measures of COVID-19 in the UK. Statistical Methods in Medical Research, 31 1757-1777 (doi:10.1177/09622802221109506). (https://arxiv.org/abs/2103.01742).
  • Englezou, Y., Waite, T.W. and Woods, D.C. (2022). Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models. Statistics and Computing, 32 82 (doi:10.1007/s11222-022-10159-2).
  • Waite T. W. and Woods D. C. (2021). Minimax-efficient random experimental design strategies with application to model-robust design for prediction. Journal of the American Statistical Association, 117 1452-1465 (doi:10.1080/01621459.2020.1863221).
  • Overstall, A. M., Woods, D. C. and Parker, B. M. (2020). Bayesian optimal design for ordinary differential equation models with application in biological science. Journal of the American Statistical Association, 115 583-598 (doi:10.1080/01621459.2019.1617154). (http://arxiv.org/abs/1509.04099).
  • Egorova, O., Hafizi, R., Woods, D. C. and Day, G. M (2020). Multifidelity statistical machine learning for molecular crystal structure prediction. Journal of Physical Chemistry A, 124 8065-8078 (doi:10.1021/acs.jpca.0c05006).
  • Rappold, A., Müller, W. G. and Woods, D. C. (2019). Copula-based robust optimal block designs. Applied Stochastic Models in Business and Industry, 36 210-219 (doi:10.1002/asmb.2469).
  • Overstall, A. M., Woods, D. C. and Martin, K. (2019). Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics. Computational Statistics and Data Analysis, 132 126-142 (doi:10.1016/j.csda.2018.10.013).
  • 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, 95 13 (doi:10.18637/jss.v095.i13). (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, 233 726-740 (doi:10.1177/1350650118794730).
  • 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
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