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Abstract Details

Activity Number: 319
Type: Invited
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #306302
Title: Bayesian Modeling for Spatial Extreme Values
Author(s): Huiyan Sang*+
Companies: Texas A&M University
Address: 416C Blocker Bldg, College Station, TX, 77845,
Keywords: Spatial extreme values ; Bayesian hierarchical modeling ; Latent Gaussian models ; MCMC

We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. We assume latent Gaussian spatial process models for the marginal GEV parameters to capture large scale dependence, and we offer a spatial process model to capture the small scale spatial dependence which is unexplained by the marginal specifications. Model inference and prediction are obtained via MCMC technique. We will also discuss modeling and computational strategies for high dimensional spatial extreme data.

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