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Activity Number: 423
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320816 View Presentation
Title: Spatial-Temporal Pareto Modeling of Extreme Value Data
Author(s): Gabriel Huerta* and Luis Enrique Nieto Barajas
Companies: University of New Mexico and Instituto Tecnologico Autonomo de Mexico
Keywords: Autoregressive model ; latent variables ; Pareto distribution ; Pollution data ; Mexico City ; stationarity
Abstract:

In this work we introduce a spatio-temporal process with Pareto marginal distributions for extreme value observations. The dependence in space and time of the process is introduced through the use of latent variables in a hierarchical fashion. For certain specifications the process becomes strictly stationary in space and time. We review the construction of the process and highlight some of its properties. We follow a Bayesian approach to estimate model parameters and produce predictions. The performance of the model is illustrated with a pollution data set of monthly maxima ozone concentrations in the metropolitan area of Mexico City. Results show that our model is superior to two other alternative models.


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