JSM 2005 - Toronto

Abstract #304025

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 93
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304025
Title: Bayesian Modeling of Multicategory Spatial Data
Author(s): Catherine Calder*+
Companies: The Ohio State University
Address: Department of Statistics Cockins Hall, Columbus, OH, 43210, United States
Keywords: data augmentation ; Markov chain Monte Carlo ; polychotomous data ; land use and land cover
Abstract:

Advances in remote sensing technologies are producing vast amounts of multicategory, or polychotomous, spatial data, which are used to study environmental and social processes on large spatial scales. Statistical methods are needed to relate such data to covariate information while accounting for and understanding the residual spatial dependence in the observed spatial process. We propose a Bayesian framework for modeling the relationship between multicategory spatial data and various types of explanatory information that relies on a latent multivariate probit structure. This latent structure allows inference on the between-category dependencies, which can provide a better understanding of the underlying mechanisms driving the spatial process. A data augmentation version of the latent variable approach enables us to use standard covariance forms for modeling spatial dependence. We illustrate our modeling framework using land use and land cover data from remote sensing images of the Oriente region in the Ecuadorian Amazon.

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Revised March 2005