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Activity Number: 593
Type: Topic Contributed
Date/Time: Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303862
Title: Multi-Scale Spatial Inference of Pollutant Concentrations
Author(s): Dawn Woodard*+
Companies: Cornell University
Address: 206 Rhodes Hall, Ithaca, NY, 14853,
Keywords: spatial ; Levy ; Bayesian ; geostatistics ; kernel mixture ; Markov chain
Abstract:

We develop a method for multi-scale estimation of pollutant concentrations and associated risk measures, based on a nonparametric Bayesian spatial model. Maps of the fine-scale estimated concentration are obtained, as well as maps of the estimated county-level average concentration and similar maps at the level of watersheds and other (non-nested) geographic regions. Other risk measures are also obtained at multiple scales, including the probability that the pollutant concentration exceeds a particular threshold. In the Bayesian context, point and interval estimates of these various quantities arise naturally from a point-level model, which we take to be a kernel mixture with Levy random field prior distribution.

Joint work with R.L. Wolpert and M.A. O'Connell.

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