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Emeric Thibaud - Colorado State University

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Bayesian inference for spatial extremes, with an application to extreme low temperatures
27 August 2015 from 4:00 PM to 5:00 PM
201 Thomas Building
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Emeric Thibaud

Abstract: Models for spatial extremes must account appropriately for asymptotic dependence, and this motivates the use of max-stable processes, which are the only non-trivial limits of properly rescaled pointwise maxima of random functions. The Brown-Resnick max-stable process has proven to be well-suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is unobtainable and inference must be based on a composite likelihood, thereby preventing the use of classical Bayesian techniques. In this talk I will describe a new approach to full likelihood inference for max-stable processes, using componentwise maxima and their partitions in terms of individual events. This approach will be illustrated by the construction of a Bayesian hierarchical model for extreme low temperatures in northern Finland. Watch Online

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