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Activity Number: 423 - Recent Advancements in the Analysis of Extremes
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #328307
Title: A Max-Infinitely Divisible Process for Sub-Asymptotic Modeling of Spatial Extremes
Author(s): Gregory Bopp* and Benjamin Shaby and Raphaƫl Huser
Companies: Pennsylvania State University and Penn State University and KAUST
Keywords: statistics of extremes; max-infinite divisibility; sub-asymptotic modeling; asymptotic dependence and independence; annual maximum analysis; Bayesian hierarchical model

Environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels. Limiting max-stable process models for extremes have a rigid dependence structure that does not capture this type of behavior. Further, full likelihood-based inference has proven to be challenging for max-stable models, and a limitation in their application to large spatial datasets. We propose a flexible model from a broader family of max-infinitely divisible processes that allows for weakening spatial dependence at increasingly extreme levels, and due to a hierarchical representation of the likelihood in terms of random effects, scales to comparatively large datasets. The proposed model is constructed using flexible kernel functions in a form that allows for straightforward inspection of the predominant patterns in the spatial distribution of extremes. In addition, the model possesses the max-stability property as a special case, making inference on the tail dependence class possible. To illustrate our method, we apply it to extreme precipitation in eastern North America.

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

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