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Abstract Details
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Activity Number:
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352
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305944 |
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Title:
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Analyzing Spatial Directional Data Using Projected Normal Processes
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Author(s):
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Fangpo Wang*+ and Alan Gelfand
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Companies:
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Duke University and Duke University
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Address:
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214 Old Chemistry Building, Durham, NC, 27708, United States
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Keywords:
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Bayesian kriging ;
Gaussian processes ;
latent variables ;
multivariate circular distribution
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Abstract:
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Directional data naturally arise in many scientific fields, such as oceanography (wave direction), meteorology (wind direction) and biology (animal migration direction). Our contribution is to develop a fully model-based approach to capture structured spatial dependence for modeling directional data at different spatial locations. We build a projected Gaussian process, induced from an inline multivariate Gaussian process. We illustrate the properties of the projected Gaussian process and show how to fit this model using suitable latent variable and Markov chain Monte Carlo methods. We also show how to implement spatial interpolation and conduct model comparison in this setting. Simulated and real data examples are provided for illustration.
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