JSM 2004 - Toronto

Abstract #301547

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Activity Number: 373
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301547
Title: Space-time Analysis of Extreme Values
Author(s): Gabriel Huerta*+ and Bruno Sanso
Companies: University of New Mexico and University of California, Santa Cruz
Address: , , 87131,
Keywords: space-time process ; extreme values ; GEV distribution ; process convolutions ; MCMC ; ozone levels
Abstract:

We propose a new approach for modeling extreme values that are measured in time and space. First we assume that the observations follow a Generalized Extreme Value (GEV) distribution for which the location, scale, or shape parameters define the space-time structure. The temporal component is defined through a Dynamic Linear Model (DLM) or state-space representation that allows to estimate the trend or seasonality of the data in time. The spatial element is imposed through the evolution matrix of the DLM where we adopt a process convolution form. We show how to produce temporal and spatial estimates of our model via customized Markov chain Monte Carlo (MCMC) simulation. We illustrate our methodology with extreme values of ozone levels produced daily in the metropolitan area of Mexico City.

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