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Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

Stochastic environmental research and risk assessment, 2018-07, Vol.32 (7), p.2179-2198

2018

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More pests but less pesticide applications: Ambivalent effect of landscape complexity on conservation biological control

PLoS computational biology, 2021-11, Vol.17 (11), p.e1009559-e1009559

2021

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What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

JMIR medical informatics, 2017-07, Vol.5 (3), p.e23-e23

2017

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High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

The annals of applied statistics, 2022-09, Vol.16 (3)

2022

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Efficient inference and simulation for elliptical Pareto processes

Biometrika, 2015-12, Vol.102 (4), p.855-870

2015

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Editorial: EVA 2021 data challenge on spatiotemporal prediction of wildfire extremes in the USA

Extremes (Boston), 2023-06, Vol.26 (2), p.241-250

Opitz, Thomas

2023

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Extreme-value modelling of migratory bird arrival dates: Insights from citizen science data

Journal of the Royal Statistical Society. Series A, Statistics in society, 2024-10

2024

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Modeling asymptotically independent spatial extremes based on Laplace random fields

Spatial statistics, 2016-05, Vol.16, p.1-18

Opitz, Thomas

2016

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Juliette Legrand and Thomas Opitz’s contribution to the Discussion of ‘The First Discussion Meeting on Statistical aspects of climate change

Journal of the Royal Statistical Society Series C: Applied Statistics, 2023-09, Vol.72 (4), p.858-859

2023

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Space-time landslide predictive modelling

Earth-science reviews, 2020-10, Vol.209, p.103318, Article 103318

2020

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Spatial modeling and future projection of extreme precipitation extents

Journal of the American Statistical Association, 2024-10, p.1-22

2024

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Extremal dependence of random scale constructions

Extremes (Boston), 2019-12, Vol.22 (4), p.623-666

2019

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Spatial extremes and stochastic geometry for Gaussian-based peaks-over-threshold processes

Extremes (Boston), 2024-09, Vol.27 (3), p.397-435

2024

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Point-process based Bayesian modeling of space–time structures of forest fire occurrences in Mediterranean France

Spatial statistics, 2020-12, Vol.40 (100429), p.100429, Article 100429

2020

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Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions

Journal of the Royal Statistical Society Series C: Applied Statistics, 2023-12, Vol.72 (5), p.1139-1161

2023

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Spatio-temporal variation and dynamic scenario simulation of ecological risk in a typical artificial oasis in northwestern China

Journal of cleaner production, 2022-10, Vol.369, p.133302, Article 133302

2022

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Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures

Spatial statistics, 2017-08, Vol.21, p.166-186

2017

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Max‐infinitely divisible models and inference for spatial extremes

Scandinavian journal of statistics, 2021-03, Vol.48 (1), p.321-348

2021

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Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning

Technometrics, 2024-07, Vol.66 (3), p.331-346

2024

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ANOVEX: ANalysis Of Variability for heavy-tailed EXtremes

Electronic journal of statistics, 2024

2024

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Spatial hierarchical modeling of threshold exceedances using rate mixtures

Environmetrics (London, Ont.), 2021-05, Vol.32 (3), p.n/a

2021

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INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles

Extremes (Boston), 2018-09, Vol.21 (3), p.441-462

2018

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Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude

The annals of applied statistics, 2022-03, Vol.16 (1)

2022

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Extremal t processes: Elliptical domain of attraction and a spectral representation: Extremal processes

Journal of multivariate analysis, 2013-11, Vol.122, p.409-413

Opitz, Thomas

2013

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Correction to: Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

The annals of applied statistics, 2024-02, Vol.18 (1)

2024

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Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

More pests but less pesticide applications: Ambivalent effect of landscape complexity on conservation biological control

What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

Efficient inference and simulation for elliptical Pareto processes

Editorial: EVA 2021 data challenge on spatiotemporal prediction of wildfire extremes in the USA

Extreme-value modelling of migratory bird arrival dates: Insights from citizen science data

Modeling asymptotically independent spatial extremes based on Laplace random fields

Juliette Legrand and Thomas Opitz’s contribution to the Discussion of ‘The First Discussion Meeting on Statistical aspects of climate change

Space-time landslide predictive modelling

Spatial modeling and future projection of extreme precipitation extents

Extremal dependence of random scale constructions

Spatial extremes and stochastic geometry for Gaussian-based peaks-over-threshold processes

Point-process based Bayesian modeling of space–time structures of forest fire occurrences in Mediterranean France

Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions

Spatio-temporal variation and dynamic scenario simulation of ecological risk in a typical artificial oasis in northwestern China

Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures

Max‐infinitely divisible models and inference for spatial extremes

Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning

ANOVEX: ANalysis Of Variability for heavy-tailed EXtremes

Spatial hierarchical modeling of threshold exceedances using rate mixtures

INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles

Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude

Extremal t processes: Elliptical domain of attraction and a spectral representation: Extremal processes

Correction to: Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

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