Spatial deformation for non-stationary extremal dependence

Richards, Jordan and Wadsworth, Jennifer (2021) Spatial deformation for non-stationary extremal dependence. Environmetrics, 32 (5): e2671. ISSN 1180-4009

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Modelling the extremal dependence structure of spatial data is considerably easier if that structure is stationary. However, for data observed over large or complicated domains, non-stationarity will often prevail. Current methods for modelling non-stationarity in extremal dependence rely on models that are either computationally difficult to fit or require prior knowledge of covariates. Sampson and Guttorp (1992) proposed a simple technique for handling non-stationarity in spatial dependence by smoothly mapping the sampling locations of the process from the original geographical space to a latent space where stationarity can be reasonably assumed. We present an extension of this method to a spatial extremes framework by considering least squares minimisation of pairwise theoretical and empirical extremal dependence measures. Along with some practical advice on applying these deformations, we provide a detailed simulation study in which we propose three spatial processes with varying degrees of non-stationarity in their extremal and central dependence structures. The methodology is applied to Australian summer temperature extremes and UK precipitation to illustrate its efficacy compared to a naive modelling approach.

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Journal Article
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Uncontrolled Keywords:
?? non-stationary spatial dependenceextremal dependencespatial deformationmax-stable processesecological modellingstatistics and probability ??
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26 Jan 2021 14:30
Last Modified:
21 May 2024 01:40