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Abstract Details
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Activity Number:
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406
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #301742 |
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Title:
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Hierarchical Bayesian Analysis of Directional Data Using the Projected Normal Distribution
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Author(s):
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Fangpo Wang*+ and Alan E. Gelfand
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Companies:
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Duke University and Duke University
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Address:
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Department of Statistical Science, Durham, NC, 27708,
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Keywords:
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circular data ;
bimodal distribution ;
mean direction ;
MCMC ;
wave direction
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Abstract:
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The projected normal distribution is an under-utilized model for explaining directional data. It provides flexibility, e.g., bimodality, asymmetry, and convenient regression specification. We develop fully Bayesian hierarchical models for circular data. We show how they can be fit using suitable latent variables and MCMC methods. We show how posterior estimation of analytical quantities such as mean direction and concentration can be implemented as well as a regression setting. Work in progress will show how we propose to build a projected Gaussian process to capture structured spatial dependence for modeling circular data at different spatial locations. Simulated and real data examples are provided for illustration.
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