| 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 | 29 | 2022 |
| Regression modelling of spatiotemporal extreme US wildfires via partially-interpretable neural networks J Richards, R Huser arXiv preprint arXiv:2208.07581, 2022 | 22* | 2022 |
| Neural Bayes estimators for irregular spatial data using graph neural networks M Sainsbury-Dale, A Zammit-Mangion, J Richards, R Huser Journal of Computational and Graphical Statistics, 1-28, 2024 | 20 | 2024 |
| Deep graphical regression for jointly moderate and extreme Australian wildfires D Cisneros, J Richards, A Dahal, L Lombardo, R Huser Spatial Statistics, 100811, 2024 | 20 | 2024 |
| Neural Bayes estimators for censored inference with peaks-over-threshold models J Richards, M Sainsbury-Dale, A Zammit-Mangion, R Huser Journal of Machine Learning Research 25 (390), 1-49, 2024 | 20* | 2024 |
| Spatial deformation for nonstationary extremal dependence J Richards, JL Wadsworth Environmetrics 32 (5), e2671, 2021 | 19 | 2021 |
| Joint estimation of extreme spatially aggregated precipitation at different scales through mixture modelling J Richards, JA Tawn, S Brown Spatial Statistics 53, 100725, 2023 | 17 | 2023 |
| 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 | 15 | 2023 |
| On the tail behaviour of aggregated random variables J Richards, JA Tawn Journal of Multivariate Analysis 192, 105065, 2022 | 6 | 2022 |
| Flexible Modeling of Nonstationary Extremal Dependence using Spatially Fused LASSO and Ridge Penalties X Shao, A Hazra, J Richards, R Huser Technometrics, 1-15, 2024 | 4 | 2024 |
| PinnEV: Partially-interpretable neural networks for modelling of extreme values J Richards R package, 2022 | 4 | 2022 |
| Deep learning of multivariate extremes via a geometric representation CJR Murphy-Barltrop, R Majumder, J Richards arXiv preprint arXiv:2406.19936, 2024 | 2 | 2024 |
| Extreme quantile regression with deep learning J Richards, R Huser arXiv preprint arXiv:2404.09154, 2024 | 2 | 2024 |
| Extremes of Aggregated Random Variables and Spatial Processes J Richards PQDT-Global, 2021 | 2 | 2021 |
| 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 |
| Deep learning joint extremes of metocean variables using the SPAR model E Mackay, C Murphy-Barltrop, J Richards, P Jonathan arXiv preprint arXiv:2412.15808, 2024 | | 2024 |
| Jordan Richards, Myung Won Lee, Viviana Carcaiso, and Miguel de Carvalho's contribution to the Discussion of “Inference for extreme spatial temperature events in a changing … J Richards, MW Lee, V Carcaiso, M de Carvalho Journal of the Royal Statistical Society Series C: Applied Statistics, qlae084, 2024 | | 2024 |
| Review of “Risk Revealed: Cautionary Tales, Understanding and Communication” by Paul Embrechts, Marius Hofert, and Valérie Chavez-Demoulin J Richards, L De Monte Journal of Agricultural, Biological and Environmental Statistics, 1-3, 2024 | | 2024 |
| Modern extreme value statistics for Utopian extremes. EVA (2023) Conference Data Challenge: Team Yalla J Richards, N Alotaibi, D Cisneros, Y Gong, MB Guerrero, PV Redondo, ... Extremes, 1-23, 2024 | | 2024 |
| The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency J Jiang, J Richards, R Huser, D Bolin arXiv preprint arXiv:2408.06661, 2024 | | 2024 |
Raphaël HuserAssociate Professor, King Abdullah University of Science and Technology (KAUST)確認したメール アドレス: kaust.edu.sa
