Thomas Opitz
Thomas Opitz
Researcher, French National Institute of Agronomic Research
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Extremal t processes: Elliptical domain of attraction and a spectral representation
T Opitz
Journal of Multivariate Analysis 122, 409-413, 2013
Efficient inference and simulation for elliptical Pareto processes
E Thibaud, T Opitz
Biometrika 102 (4), 855-870, 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
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
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
Space-time landslide predictive modelling
L Lombardo, T Opitz, F Ardizzone, F Guzzetti, R Huser
Earth-science reviews 209, 103318, 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
Modeling asymptotically independent spatial extremes based on Laplace random fields
T Opitz
Spatial Statistics 16, 1-18, 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
Extremal dependence of random scale constructions
S Engelke, T Opitz, J Wadsworth
Extremes 22 (4), 623-666, 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
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
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
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
Max‐infinitely divisible models and inference for spatial extremes
R Huser, T Opitz, E Thibaud
Scandinavian Journal of Statistics 48 (1), 321-348, 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
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
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
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
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
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