Publications by Raphaël Huser

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Max-stable processes are the natural analogues of the generalized extreme-value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replicati ...

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 ...

The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high t ...

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 ...

A common problem in spatial statistics is to predict a random field f at some spatial location t(0) using observations f(t(1)),..., f(t(n)) at t(1),..., t(n) epsilon IRd. Recent work by Kaufman et al. and Furrer et al. studies the use of tapering for reduc ...

Genton et al. (2011) investigated the gain in efficiency when triplewise, rather than pairwise, likelihood is used to fit the popular Smith max-stable model for spatial extremes. We generalize their results to the Brown--Resnick model and show that the eff ...

Raphaël Huser

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Space-time modelling of extreme events

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

Likelihood estimators for multivariate extremes

Geostatistics of Dependent and Asymptotically Independent Extremes

Nonstationary Positive Definite Tapering On The Plane

Composite likelihood estimation for the Brown--Resnick process