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Activity Number: 352
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305944
Title: Analyzing Spatial Directional Data Using Projected Normal Processes
Author(s): Fangpo Wang*+ and Alan Gelfand
Companies: Duke University and Duke University
Address: 214 Old Chemistry Building, Durham, NC, 27708, United States
Keywords: Bayesian kriging ; Gaussian processes ; latent variables ; multivariate circular distribution

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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