This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 52
Type: Invited
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Computing
Abstract - #305996
Title: Hierarchical Spatial Models for Predicting Forest Variables Over Large Heterogeneous Domains
Author(s): Sudipto Banerjee*+ and Andrew Finley
Companies: University of Minnesota and Michigan State University
Address: 420 Delaware St SE, MMC-303, Minneapolis, MN, 55455,
Keywords: Biomass ; Coefficient processes ; Hierarchical spatial models ; Markov chain Monte Carlo ; Multivariate spatial process ; Spatial predictive process

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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