| Modelling extremes of spatial aggregates of precipitation using conditional methods J Richards, JA Tawn, S Brown The Annals of Applied Statistics 16 (4), 2693-2713, 2022 | 21 | 2022 |
| Spatial deformation for nonstationary extremal dependence J Richards, JL Wadsworth Environmetrics 32 (5), e2671, 2021 | 15 | 2021 |
| Regression modelling of spatiotemporal extreme US wildfires via partially-interpretable neural networks J Richards, R Huser arXiv preprint arXiv:2208.07581, 2022 | 14* | 2022 |
| Joint estimation of extreme spatially aggregated precipitation at different scales through mixture modelling J Richards, JA Tawn, S Brown Spatial Statistics 53, 100725, 2023 | 11 | 2023 |
| Deep graphical regression for jointly moderate and extreme Australian wildfires D Cisneros, J Richards, A Dahal, L Lombardo, R Huser Spatial Statistics, 100811, 2024 | 10 | 2024 |
| Insights into the drivers and spatiotemporal trends of extreme mediterranean wildfires with statistical deep learning J Richards, R Huser, E Bevacqua, J Zscheischler Artificial Intelligence for the Earth Systems 2 (4), e220095, 2023 | 9 | 2023 |
| Neural Bayes estimators for irregular spatial data using graph neural networks M Sainsbury-Dale, J Richards, A Zammit-Mangion, R Huser arXiv preprint arXiv:2310.02600, 2023 | 5 | 2023 |
| Likelihood-free neural Bayes estimators for censored inference with peaks-over-threshold models J Richards, M Sainsbury-Dale, A Zammit-Mangion, R Huser arXiv preprint arXiv:2306.15642, 2023 | 5 | 2023 |
| PinnEV: Partially-interpretable neural networks for modelling of extreme values J Richards R package, 2022 | 5 | 2022 |
| On the tail behaviour of aggregated random variables J Richards, JA Tawn Journal of Multivariate Analysis 192, 105065, 2022 | 4 | 2022 |
| Flexible modeling of nonstationary extremal dependence using spatially-fused LASSO and ridge penalties X Shao, A Hazra, J Richards, R Huser arXiv preprint arXiv:2210.05792, 2022 | 4 | 2022 |
| Partially interpretable neural networks for high-dimensional extreme quantile regression: With application to wildfires within the Mediterranean Basin J Richards, R Huser, E Bevacqua, J Zscheischler EGU General Assembly Conference Abstracts, EGU22-2179, 2022 | 1 | 2022 |
| Extremes of Aggregated Random Variables and Spatial Processes J Richards PQDT-Global, 2021 | 1 | 2021 |
| Extreme quantile regression with deep learning J Richards, R Huser arXiv preprint arXiv:2404.09154, 2024 | | 2024 |
| Deep Compositional Models for Nonstationary Extremal Dependence X Shao, J Richards, R Huser 2023 IMS International Conference on Statistics and Data Science (ICSDS), 637, 2023 | | 2023 |
| Modern extreme value statistics for Utopian extremes J Richards, N Alotaibi, D Cisneros, Y Gong, MB Guerrero, P Redondo, ... arXiv preprint arXiv:2311.11054, 2023 | | 2023 |
| Insights into the drivers and spatio-temporal trends of extreme wildfires with statistical deep-learning J Richards, R Huser EGU General Assembly Conference Abstracts, EGU-2332, 2023 | | 2023 |
| Partially-interpretable neural networks for high-dimensional extreme quantile regression: With application to US wildfires J Richards, R Huser | | 2022 |
| Jbrich95/pinnEV: Partially-Interpretable Neural Networks for Extreme Value modelling J Richards, R Huser Github, 2022 | | 2022 |
| Modelling the tail behaviour of precipitation aggregates using conditional spatial extremes. J Richards, J Tawn, S Brown EGU21, 2021 | | 2021 |
Raphaël HuserAssociate Professor, King Abdullah University of Science and Technology (KAUST)Verifierad e-postadress på kaust.edu.sa
