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Activity Number:
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288
- Advances in Nonlinear and Non-Gaussian Spatio-Temporal Dynamical Models for Environmental and Ecological Processes
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #324068
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Title:
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Spatial-Temporal Modeling of Heavy Tailed Data
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Author(s):
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Gabriel Huerta*
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Companies:
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University of New Mexico
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Keywords:
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Spatio-temporal ;
Non-Gaussian ;
Heavy-tailed ;
Pareto distribution ;
GEV/GPD distributions ;
Latent variables
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Abstract:
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In this work we introduce a spatio-temporal process starting with a specific that allows for Pareto marginal distributions. Dependence in space and time is introduced through the use of latent variables in a hierarchical fashion. We review the construction of the process and study some of its properties. We follow a Bayesian approach to estimate the model and show how to obtain posterior inference via MCMC methods. The performance of the process is illustrated with a pollution dataset of monthly maxima ozone concentration and compared to other alternative models. Following on the work of Gaetan and Bortot (2014), we also consider generalizations of this model that allow for other marginal distributions and other representations at the latent level.
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Authors who are presenting talks have a * after their name.
