Abstract Details
Activity Number:
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91
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Type:
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Invited
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Date/Time:
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Sunday, August 9, 2015 : 9:30 PM to 10:15 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #316322
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Title:
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A Gauss-Pareto Process Model for Spatial Prediction of Extreme Precipitation
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Author(s):
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Robert Alohimakalani Yuen* and Peter Guttorp
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Companies:
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University of Michigan and University of Washington
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Keywords:
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spatial ;
extremes ;
precipitation ;
prediction ;
censoring
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Abstract:
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In order to develop adaptive strategies for dealing with consequences of extreme precipitation such as insufficient drainage and various aspects of flooding, it is necessary to be able to estimate extremes at unobserved sites. We introduce a hierarchical Gauss-Pareto model for spatial prediction of precipitation given nearby observations that are extreme. The model belongs to the max-domain of attraction of popular Brown-Resnick max-stable processes and retains the essential dependence structure of their corresponding generalized Pareto processes. An MCMC algorithm is developed for inference. The algorithm allows for left censored data from precipitation that accumulates below instrument precision, which often ocurrs despite nearby observations that are extreme. The model and methodology is applied to summer 24 hour cumulative precipitation over south central Sweden.
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Authors who are presenting talks have a * after their name.
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