contact details

    Dr Benjamin D. Youngman
    Lecturer
    Statistical Science
    College of Engineering, Mathematics and Physical Sciences
    Laver Building
    University of Exeter
    Exeter, UK

    research overview

    I am a Lecturer in the Statistical Science group at the University of Exeter. My research has recently been supported by the Willis Research Network and based on statistical methods for extreme values, typically applied to the modelling of natural hazards. In 2016 I won the Lloyd's Science of Risk Prize for a paper on a geostatistical extreme-value framework for fast simulation of natural hazard events.

    news

  • evgam v0.1.4 is now available on CRAN: https://CRAN.R-project.org/package=evgam
  • A preprint on some of its uses is available on arXiv: arXiv:2003.04067
  • phd topics

  • If you're interested in a PhD on any of the topics below, do get in touch.
    • Statistical modelling of extreme values and events
    • Spatial statistics
    • Natural hazards
    • Parametric insurance
  • publications

    preprints

  • Youngman, B. D. (2020). Flexible models for nonstationary dependence: Methodology and examples. In Revision. arXiv:2001.06642.
  • 2020

  • Youngman, B. D. (2020). evgam: An R package for Generalized Additive Extreme Value Models. Journal of Statistical Software. Accepted. arXiv:2003.04067.
  • Xiong X., Economou T. and Youngman B. D. (2020) Data fusion with Gaussian processes for estimation of environmental hazard events. Environmetrics. 2020;e2660. DOI: 10.1002/env.2660. To Appear.
  • 2019

  • Youngman, B. D. (2019). Generalized additive models for exceedances of high thresholds with an application to return level estimation for US wind gusts. Journal of the American Statistical Association 114(528), 1865–1879 DOI: 10.1080/01621459.2018.1529596.
  • 2018

  • Figueiredo, R., M. L. Martina, D. B. Stephenson, and B. D. Youngman (2018). A probabilistic paradigm for the parametric insurance of natural hazards. Risk Analysis 38(11), 2400–2414. DOI: 10.1111/risa.13122.
  • Khare, S., Z. Chalabi, and B. Youngman (2018). Spatio-temporal distribution of historical extreme winter temperatures in England and Scotland | a non-stationary extreme value analysis. Journal of Extreme Events 05 (01), 1750005. DOI: 10.1142/S2345737617500051.
  • 2017

  • Oakley, J. E. and B. D. Youngman (2017). Calibration of stochastic computer simulators using likelihood emulation. Technometrics 59 (1), 80-92. DOI: 10.1080/00401706.2015.1125391.
  • Youngman, B. D. and T. Economou (2017). Generalised additive point process models for natural hazard occurrence. Environmetrics 28 (4), e2444. DOI: 10.1002/env.2444.
  • Stephenson, D. B., A. Hunter, B. Youngman, and I. Cook (2017). Chapter 3 -towards a more dynamical paradigm for natural catastrophe risk modeling. In G. Michel (Ed.), Risk Modeling for Hazards and Disasters, pp. 63-77. Elsevier. DOI: 10.1016/C2015-0-01065-6.
  • 2016

  • Youngman, B. D. and D. B. Stephenson (2016). A geostatistical extreme-value framework for fast simulation of natural hazard events. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 472 (2189). DOI: 10.1098/rspa.2015.0855
  • 2014

  • Roberts, J., A. Champion, L. Dawkins, K. Hodges, L. Shaffrey, D. Stephenson, M. Stringer, H. Thornton, and B. Youngman (2014). The XWS open access catalogue of extreme European windstorms from 1979 to 2012. Nat. Hazards Earth Syst. Sci 14, 2487-2501. DOI: 10.5194/nhess-14-2487-2014
  • software

  • evgam

    I have created the evgam R package for generalised additive extreme-value models. It's currently under development, but source code is provided with some examples.

  • ppgam

    Theo Economou and I developed similar - yet slightly more basic - R code for generalised additive point process models, which accompanies Youngman and Economou (2017). Source code is provided with some examples.

  • recalibrate

    This is a fairly simple R package for recalibrating spatial fields using observations and model output. The idea is that a spatial process has some true values that we want to infer from imperfect data. The code is motivated by, but generalises beyond, European windstorms.

  • teaching

  • I will teach week 7 onwards of semester 2 of MTH2006: Statistical Modelling and Inference. Notes for the module can be found on its ELE page.
  • I am module convenor for MTHM044: Master's Project in Statistics. Here's the module's ELE page.