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Abstract Details

Activity Number: 319
Type: Invited
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #306047
Title: Nonparametric Spatial Models for Extreme Temperature Data
Author(s): Montserrat Fuentes*+ and John Henry and Brian Reich
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Address: Statistics Department, 8203 NCSU box, Raleigh, NC, 27695, USA
Keywords: spatial extremes ; nonparametric spatial models ; temperature data ; nonstationary extremes ; Bayesian inference

Estimating the probability of extreme temperature events is difficult because of limited records across time and the need to extrapolate the distributions of these events. In this paper, in order to make inference about spatial extreme events, we introduce a new nonparametric model for extremes. We present a Dirichlet-based copula model that is a flexible alternative to parametric copula models such as the normal and t-copula. This presents the most flexible multivariate copula approach in the literature. The proposed modelling approach is fitted using a Bayesian framework that allow us to take into account different sources of uncertainty in the data and models.To characterize the complex dependence structure in the extreme events we use nonstationary (space-dependent) extremal coefficient functions. We apply our methods to annual maximum temperature values in United States.

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