Non-Stationary Dependence Structures for Spatial Extremes

By Raphaël Huser, Marc G. Genton. 2014

86 citations recorded by the source corpus.

Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.

Subfields: Environmental Engineering, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/caf2f5a4edfbbd35b92078aa151dbf1a08992d05

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Finance

In Statistics and Probability

Citations in the corpus

Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures (2016, background): “Modeling of non-stationarity (Huser and Genton, 2016) and space-time dependence (Huser and Davison, 2014) is an important aspect in statistics of extremes; it would be interesting to investigate useful extensions of Gaussian scale mixture models in further research.”
Factor Copula Models for Replicated Spatial Data (2015, methodology): “…upper tail dependence function (Segers 2012) of the limiting extreme-value copula in the exponential factor model (Example 2) corresponds to the Hüsler and Reiss (1989) distribution, which has been widely used as a flexible family to model extreme events (Davison, Padoan, and Ribatet 2012;…”