JSM 2004 Online Program

Abstract #300808

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Activity Number:	272
Type:	Contributed
Date/Time:	Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #300808
Title:	Gaussian Process Models for the Sphere
Author(s):	Margaret B. Short*+
Companies:	Los Alamos National Laboratory
Address:	3262 Walnut A, Los Alamos, NM, 87544,
Keywords:	spatial process ; process convolutions ; spherical model ; Markov chain Monte Carlo ; atmospheric science
Abstract:	Spatial processes are often described with a Gaussian process model, which may be modeled via the convolution of a white noise process and a smoothing kernel. Usually, a Cartesian coordinate system (flat-earth assumption) is employed. We explore the validity of this assumption by defining convolution processes on the sphere and compare various compactly supported kernels against their flat earth counterparts. Ours is a Bayesian approach which uses Markov chain Monte Carlo (MCMC) for estimation. We illustrate using examples from atmospheric science.

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