An extended Gaussian max-stable process model for spatial extremes |
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Authors: | Elizabeth L. Smith Alec G. Stephenson |
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Affiliation: | 1. Department of Statistics and Applied Probability, National University of Singapore, Singapore 11756;2. Faculty of Life and Social Sciences, Hawthorn Campus, Swinburne University of Technology, VIC 3122, Australia |
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Abstract: | The extremes of environmental processes are often of interest due to the damage that can be caused by extreme levels of the processes. These processes are often spatial in nature and modelling the extremes jointly at many locations can be important. In this paper, an extension of the Gaussian max-stable process is developed, enabling data from a number of locations to be modelled under a more flexible framework than in previous applications. The model is applied to annual maximum rainfall data from five sites in South-West England. For estimation we employ a pairwise likelihood within a Bayesian analysis, incorporating informative prior information. |
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Keywords: | Bayesian modelling Extreme value theory Max-stable processes Multivariate extreme value theory Spatial extremes |
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