JSM 2005 - Toronto

Abstract #303775

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 508
Type: Topic Contributed
Date/Time: Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303775
Title: Approximately Optimal Spatial Design Approaches for Environmental Health Data
Author(s): Gangqiang Xia*+ and Alan E. Gelfand and Marie L. Miranda
Companies: Duke University and Duke University and Duke University
Address: ISDS, Old Chem. Bldg., Durham, NC, 27708, United States
Keywords: Gaussian process ; Fisher information ; sequential sampling ; stochastic search
Abstract:

Environmental health research considers the relationship between exposures to environmental contaminants and particular health endpoints. In our paper, we envision exposure surfaces as conceptually measurable at every location in a study region, and we develop an "optimal" model-based sampling strategy to learn about the spatial distribution of contaminants. We propose a sequential sampling design based on information criterion under usual Gaussian spatial process model assumptions. Stochastic search and block design also are discussed. We present some theoretical and empirical properties and relationships among these strategies and provide an illustrative implementation for a simulated dataset.

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Revised March 2005