| The XWS open access catalogue of extreme European windstorms from 1979 to 2012 JF Roberts, AJ Champion, LC Dawkins, KI Hodges, LC Shaffrey, ... Natural Hazards and Earth System Sciences 14 (9), 2487-2501, 2014 | 154 | 2014 |
| Generalized additive models for exceedances of high thresholds with an application to return level estimation for US wind gusts BD Youngman Journal of the American Statistical Association, 2019 | 54 | 2019 |
| evgam: An R package for generalized additive extreme value models BD Youngman arXiv preprint arXiv:2003.04067, 2020 | 26 | 2020 |
| A probabilistic paradigm for the parametric insurance of natural hazards R Figueiredo, MLV Martina, DB Stephenson, BD Youngman Risk Analysis 38 (11), 2400-2414, 2018 | 25 | 2018 |
| Calibration of stochastic computer simulators using likelihood emulation J Oakley, B Youngman Technometrics, 2015 | 24 | 2015 |
| A geostatistical extreme-value framework for fast simulation of natural hazard events B Youngman, D Stephenson Proceeding of the Royal Society of London A 472 (2189), 2016 | 23 | 2016 |
| Generalised additive point process models for natural hazard occurrence BD Youngman, T Economou Environmetrics 28 (4), e2444, 2017 | 17 | 2017 |
| New extreme rainfall projections for improved climate resilience of urban drainage systems SC Chan, EJ Kendon, HJ Fowler, BD Youngman, M Dale, C Short Climate Services 30, 100375, 2023 | 8 | 2023 |
| evgam: Generalised Additive Extreme Value Models B Youngman R package version 0.1 4, 2020 | 5 | 2020 |
| Calibration of complex computer simulators using likelihood emulation JE Oakley, BD Youngman arXiv preprint arXiv:1403.5196, 2014 | 4 | 2014 |
| Predictability of European winter 2020/2021: Influence of a mid‐winter sudden stratospheric warming JF Lockwood, N Stringer, HE Thornton, AA Scaife, PE Bett, T Collier, ... Atmospheric Science Letters 23 (12), e1126, 2022 | 2 | 2022 |
| Flexible models for nonstationary dependence: Methodology and examples BD Youngman arXiv preprint arXiv:2001.06642, 2020 | 2 | 2020 |
| Data fusion with Gaussian processes for estimation of environmental hazard events X Xiong, BD Youngman, T Economou Environmetrics 32 (3), e2660, 2021 | 1 | 2021 |
| Towards a more dynamical paradigm for natural catastrophe risk modeling DB Stephenson, A Hunter, B Youngman, I Cook Risk modeling for hazards and disasters, 63-77, 2018 | 1 | 2018 |
| deform: An R Package for Nonstationary Spatial Gaussian Process Models by Deformations and Dimension Expansion BD Youngman arXiv preprint arXiv:2311.05272, 2023 | | 2023 |
| Providing future UK heavy precipitation guidance for water management stakeholders using a convection-permitting climate model ensemble and a spatial extreme statistical model S Chan, E Kendon, B Youngman, G Fosser, C Short, H Fowler, S Tucker, ... EGU General Assembly Conference Abstracts, EGU21-726, 2021 | | 2021 |
| Spatial inference for hazard event intensities using imperfect observation and simulation data BD Youngman, DB Stephenson | | 2019 |
| A statistical, machine learning framework for parametric risk transfer. MLV Martina, R Figueiredo, DB Stephenson, BD Youngman Geophysical Research Abstracts 21, 2019 | | 2019 |
| A probabilistic strategy for parametric catastrophe insurance R Figueiredo, M Martina, D Stephenson, B Youngman EGU General Assembly Conference Abstracts, 15373, 2017 | | 2017 |
| Inference for spatial processes using imperfect data from measurements and numerical simulations B Youngman, D Stephenson arXiv, 2016 | | 2016 |
