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Activity Number:
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298
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #301418 |
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Title:
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Selection of Spatial Correlation Structures with Bayesian Model-averaging with Application to Loblolly Pines
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Author(s):
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Edward L. Boone*+ and Bronson Bullock
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Companies:
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University of North Carolina, Wilmington and North Carolina State University
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Address:
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Dept. of Mathematics and Statistics, Wilmington, NC, 28403,
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Keywords:
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spatial statistics ; Bayesian model-averaging ; forestry
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Abstract:
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Many applications of statistical methods for data that are spatially correlated require the researcher to specify the correlation structure of the data. This can be a difficult task due to the fact that there are many candidate structures. Some spatial correlation structures depend on the distance between the observed data points while others rely on neighborhood structures. Bayesian methods that systematically determine the "best" correlation structure from a predefined class of structures are proposed. Bayes factors, highest probability models, and Bayesian model-averaging are employed to determine the "best" correlation structure and to average across these structures to create a nonparametric alternative structure. An application is given for a loblolly pine dataset with known coordinates. Tree diameters and heights were measured and an investigation into the spatial dependence between the trees was conducted. Results showed that the most probable model for the spatial correlation structure agreed with allometric trends for loblolly pine.
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