Modeling Spatial Processes with Unknown Extremal Dependence Class
Modeling Spatial Processes with Unknown Extremal Dependence Class.pdf
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/626518
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AbstractMany environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, that is, the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure. Supplementary materials for this article are available online.
CitationHuser R, Wadsworth JL (2018) Modeling Spatial Processes with Unknown Extremal Dependence Class. Journal of the American Statistical Association: 1–11. Available: http://dx.doi.org/10.1080/01621459.2017.1411813.
SponsorsThe authors thank Philip Jonathan of Shell Research for the wave height data analyzed in Section 4.1, and Raphaël de Fondeville for helpful discussions and code for multivariate Gaussian computation.
PublisherInforma UK Limited
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.