Abstract Details
Activity Number:
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299
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #309188 |
Title:
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Spatial Graphical Model for High-Dimensional Discrete Lattices
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Author(s):
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Xuan Che*+ and Alix I. Gitelman
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Companies:
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Oregon State University and Department of Statistics, Oregon State University
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Keywords:
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graphical model ;
spatial statistics ;
Bayesian inference ;
Markov chain Monte Carlo
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Abstract:
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The amount and dimensionality of spatial datasets have increased dramatically thanks to advancements of data collection tools. We consider the case of multivariate observations collected on a lattice, of which remotely sensed data provides a key example. For the situation where some of the components of the multivariate observation are discrete, we develop methods for specifying the multivariate joint distribution as a chain graph with both discrete and continuous components, and with spatial dependencies assumed among all variables on the lattice. We propose a new group of chain graphs, generalized tree networks, and, by constructing the chain graph as a generalized tree network, partition its joint distribution according to the maximal cliques of the graph. We then use a Gaussian Copula transformation to model spatial dependence among the discrete variables in the cliques. We demonstrate our method using simulated data and also apply it to a remote sensing dataset.
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