This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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52
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Type:
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Invited
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Date/Time:
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Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #305996 |
Title:
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Hierarchical Spatial Models for Predicting Forest Variables Over Large Heterogeneous Domains
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Author(s):
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Sudipto Banerjee*+ and Andrew Finley
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Companies:
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University of Minnesota and Michigan State University
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Address:
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420 Delaware St SE, MMC-303, Minneapolis, MN, 55455,
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Keywords:
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Biomass ;
Coefficient processes ;
Hierarchical spatial models ;
Markov chain Monte Carlo ;
Multivariate spatial process ;
Spatial predictive process
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Abstract:
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We are interested in predicting one or more continuous forest variables at a fine resolution across a specified domain. Given a definition of forest/non-forest, this prediction is typically a two step process. The first step predicts which locations are forested. The second step predicts the value of the variable for only those forested locations. Rarely is the forest/non-forest predicted without error. Here we envision two latent processes generating the data: a continuous spatial process to model the forest attribute conditional upon a binary spatial process indicating ``measurable'' attribute. Flexibility is achieved by modeling the regression coefficients as unknown functions of space. The proposed models are motivated using geo-referenced National Forest Inventory (NFI) data and coinciding remotely sensed predictor variables. Computing issues for large datasets are also addressed.
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