Emeric Rolland Georges Thibaud

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For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max-stable models are inadequate to capture the rate of joint tail decay, and to estimate jo ...

Motivated by the widespread use of large gridded data sets in the atmospheric sciences, we propose a new model for extremes of areal data that is inspired by the simultaneous autoregressive (SAR) model in classical spatial statistics. Our extreme SAR model ...

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect depen ...

Spatial modeling of rare events has obvious applications in the environmental sciences and is crucial when assessing the effects of catastrophic events (such as heatwaves or widespread flooding) on food security and on the sustainability of societal infras ...

Species distribution models are increasingly used to address conservation questions, so their predictive capacity requires careful evaluation. Previous studies have shown how individual factors used in model construction can affect prediction. Although som ...

We present a method for drawing isolines indicating regions of equal joint exceedance probability for bivariate data. The method relies on bivariate regular variation, a dependence framework widely used for extremes. The method we utilize for characterizin ...

We propose two decompositions that help to summarize and describe high-dimensional tail dependence within the framework of regular variation. We use a transformation to define a vector space on the positive orthant and show that transformed-linear operatio ...

The Brown-Resnick max-stable process has proven to be well suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preve ...

This thesis proposes some contributions to the spatial modelling of species distributions and extreme values. Predictive models are increasingly used to model the distribution of species and to estimate the potential effects of global change on biodiversit ...

Statistical methods for inference on spatial extremes of large datasets are yet to be developed. Motivated by standard dimension reduction techniques used in spatial statistics, we propose an approach based on empirical basis functions to explore and model ...

We propose an approach to spatial modeling of extreme rainfall, based on max-stable processes fitted using partial duration series and a censored threshold likelihood function. The resulting models are coherent with classical extreme-value theory and allow ...

Emeric Rolland Georges Thibaud

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Max-infinitely divisible models and inference for spatial extremes

Simultaneous autoregressive models for spatial extremes

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

Geostatistics of Dependent and Asymptotically Independent Extremes

Measuring the relative effect of factors affecting species distribution model predictions

A nonparametric method for producing isolines of bivariate exceedance probabilities

Decompositions of dependence for high-dimensional extremes

Bayesian Inference For The Brown-Resnick Process, With An Application To Extreme Low Temperatures

Contributions to Spatial Statistics : Species Distributions and Rare Events

Exploration and Inference in Spatial Extremes Using Empirical Basis Functions

Threshold modeling of extreme spatial rainfall