| Extremal t processes: Elliptical domain of attraction and a spectral representation T Opitz Journal of Multivariate Analysis 122, 409-413, 2013 | 151 | 2013 |
| Efficient inference and simulation for elliptical Pareto processes E Thibaud, T Opitz Biometrika 102 (4), 855-870, 2015 | 106 | 2015 |
| Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures R Huser, T Opitz, E Thibaud Spatial Statistics 21, 166-186, 2017 | 95 | 2017 |
| Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L Lombardo, T Opitz, R Huser Stochastic environmental research and risk assessment 32, 2179-2198, 2018 | 87 | 2018 |
| INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles T Opitz, R Huser, H Bakka, H Rue Extremes 21, 441-462, 2018 | 84 | 2018 |
| Space-time landslide predictive modelling L Lombardo, T Opitz, F Ardizzone, F Guzzetti, R Huser Earth-science reviews 209, 103318, 2020 | 83 | 2020 |
| What patients can tell us: topic analysis for social media on breast cancer MDT Nzali, S Bringay, C Lavergne, C Mollevi, T Opitz JMIR medical informatics 5 (3), e7779, 2017 | 77 | 2017 |
| Modeling asymptotically independent spatial extremes based on Laplace random fields T Opitz Spatial Statistics 16, 1-18, 2016 | 63 | 2016 |
| Latent Gaussian modeling and INLA: A review with focus on space-time applications T Opitz Journal de la société française de statistique 158 (3), 62-85, 2017 | 36 | 2017 |
| Extremal dependence of random scale constructions S Engelke, T Opitz, J Wadsworth Extremes 22 (4), 623-666, 2019 | 31 | 2019 |
| Point-process based Bayesian modeling of space–time structures of forest fire occurrences in Mediterranean France T Opitz, F Bonneu, E Gabriel Spatial Statistics 40, 100429, 2020 | 30 | 2020 |
| Hierarchical space-time modeling of asymptotically independent exceedances with an application to precipitation data JN Bacro, C Gaetan, T Opitz, G Toulemonde Journal of the American Statistical Association 115 (530), 555-569, 2020 | 30 | 2020 |
| Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood F Pimont, H Fargeon, T Opitz, J Ruffault, R Barbero, N Martin‐StPaul, ... Ecological applications 31 (5), e02316, 2021 | 29 | 2021 |
| Numerical recipes for landslide spatial prediction using R-INLA: a step-by-step tutorial L Lombardo, T Opitz, R Huser Spatial modeling in GIS and R for earth and environmental sciences, 55-83, 2019 | 26 | 2019 |
| Max‐infinitely divisible models and inference for spatial extremes R Huser, T Opitz, E Thibaud Scandinavian Journal of Statistics 48 (1), 321-348, 2021 | 25 | 2021 |
| Detecting and modeling multi-scale space-time structures: the case of wildfire occurrences E Gabriel, T Opitz, F Bonneu Journal de la Société Française de Statistique 158 (3), 86-105, 2017 | 19 | 2017 |
| Breast cancer and quality of life: medical information extraction from health forums T Opitz, J Azé, S Bringay, C Joutard, C Lavergne, C Mollevi 25th European Medical Informatics Conference (MIE) 20 (205), 1070-1074, 2014 | 18 | 2014 |
| Spatiotemporal wildfire modeling through point processes with moderate and extreme marks J Koh, F Pimont, JL Dupuy, T Opitz The annals of applied statistics 17 (1), 560-582, 2023 | 16 | 2023 |
| Analyzing spatio-temporal data with R: Everything you always wanted to know–but were afraid to ask R Network Journal de la Société Française de Statistique 158 (3), 124-158, 2017 | 16 | 2017 |
| Index for predicting insurance claims from wind storms with an application in France A Mornet, T Opitz, M Luzi, S Loisel Risk Analysis 35 (11), 2029-2056, 2015 | 15 | 2015 |
