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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

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.

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

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