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Abstract Details
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Activity Number:
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490
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #305680 |
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Title:
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Time-Varying and Spatial Modeling of Precipitation Extremes
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Author(s):
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Gabriel Huerta*+ and Glenn Stark
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Companies:
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Indiana University and University of New Mexico
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Address:
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309 N. Park Ave., Bloomington, IN, 47408, USA
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Keywords:
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GEV distribution ;
Precipitation extremes ;
Predictive distributions ;
Gauss-Markov fields ;
MCMC ;
Hierarchical models
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Abstract:
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We introduce some novel approaches for extremes value analysis that rely on Bayesian dynamic linear modeling and intrinsic Gauss-Markov Random Fields. In particular, we characterize extreme precipitation from a regional climate model via a hierarchical structure based on the Generalized Extreme Value distribution (GEV) that assigns a latent spatial process to its location and scale parameters. In addition, the statistical modeling includes an annual shift in the location parameter that may be able to predict trends over time. Some portion of the available output was held out and treated as missing data, allowing for an evaluation of the model capability to predict extreme precipitation events.
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