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Activity Number: 121 - SPEED: Environmental Statistics
Type: Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #322786 View Presentation
Title: Modeling Spatial Extremes Using Positive Stable Mixtures
Author(s): Gregory Bopp*
Companies:
Keywords: Extreme Value Theory ; Mixture Model ; Bayesian Model ; MCMC ; Extremal dependence ; Max-stable
Abstract:

Max-stable processes constitute a broad class of models for capturing spatial dependence among extremes, a feature common to many environmental phenomena. A drawback of these models is that the likelihood for max-stable process is unavailable for more than a small number of spatial locations, precluding Bayesian inference. The proposed hierarchical Bayesian model uses positive stable random effects to model residual spatial dependence and takes the form of two popular max-stable families as limiting cases.


Authors who are presenting talks have a * after their name.

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