Abstract #300185

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JSM 2003 Abstract #300185
Activity Number: 445
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #300185
Title: Point Source Modeling Using a Bayesian Hierarchical Nonstationary Spatial Model
Author(s): Tae-Young Heo*+ and Jacqueline M. Hughes-Oliver and Sujit Kumar Ghosh
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Address: Department of Statistics, Raleigh, NC, 27695-8203,
Keywords: autoregressive process ; conditional autoregressive ; correlation nonstationarity ; deviance information criterion ; positive definite ; random effects
Abstract:

We suggest a parametric modeling approach for nonstationary spatial processes driven by point sources. Baseline stationarity, which may be reasonable in the absence of a point source, is modeled using a conditional autoregressive Markov random field. Variability due to the point source is captured by our so-called autoregressive point source model. Inference proceeds according to a Bayesian hierarchical paradigm, and is implemented using Markov chain Monte Carlo in WinBUGS. The parametric approach allows a formal test of effectiveness of the source. Application is made to a real dataset on electromagnetism measurements in a field containing a metal pole.

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