Spatial deformation for non-stationary extremal dependence
Autor: | Richards, Jordan, Wadsworth, Jennifer L. |
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Rok vydání: | 2021 |
Předmět: | |
Zdroj: | Environmetrics, e2671 (2021) |
Druh dokumentu: | Working Paper |
DOI: | 10.1002/env.2671 |
Popis: | 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. Comment: 41 pages, 10 figures |
Databáze: | arXiv |
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