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A Moving Average Bayesian Model for Spatial Surface and Coverage Prediction from Environmental Point-Source Data

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Abstract:This paper addresses the general problem of estimating at arbitrary locations the value of an unobserved quantity that varies over space, such as ozone concentration in air or nitrate concentrations in surface groundwater, on the basis of approximate measurements of the quantity and perhaps of associated covariates at specificied locations. A nonparametric Bayesian approach is proposed, in which a joint prior distribution for the unobserved spatially-varying quantity is constructed as a moving average of independent-increment random measures. A reversible jump Markov chain Monte Carlo computational approach is proposed for approximating the posterior distribution of the unobserved quantity at all spatial locations, as well as averages of the quantity over arbitrary regions and other summaries of interest. The moving average Bayesian approach is compared with more conventional nitrate concentrations in groundwater. The surfaces and coverages are intended for use as part of the Regional Vulnerability Assessment (ReVA) program in the mid-Atlantic region.
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Citation:Wolpert, R. L., E. R. Smith, and M. O'connell. A Moving Average Bayesian Model for Spatial Surface and Coverage Prediction from Environmental Point-Source Data. Presented at US EPA 23rd Annual National Conference on Managing Environmental Quality Systems, Tampa, FL, April 13-16, 2004.
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Contact: Chris Siebert - (702) 798-2234 or siebert.christopher@epa.gov
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Division: Environmental Sciences Division
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Branch: Landscape Characterization Branch (RTP)
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Product Type: Abstrct/Oral
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Presented: 04/13/2004
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Related Entries:
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Bullet Item Regional Scale Comparative Risk Assessment
spacer Relationship Reason:   A Project of the Product
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Last Updated on Monday, October 22, 2007
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