Abstract Details
Activity Number:
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182
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #316174
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View Presentation
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Title:
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Joint Spatio-Temporal Analysis of a Linear and a Directional Variable: Space-Time Modeling of Wave Heights and Wave Directions
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Author(s):
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Fangpo Wang* and Alan Gelfand and Giovanna Jona-Lasinio
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Companies:
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Adobe Systems, Inc. and Duke University and Sapienza University of Rome
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Keywords:
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Angular variable ;
Bayesian kriging ;
hierarchical model ;
latent variables ;
projected Gaussian process ;
Markov chain Monte Carlo
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Abstract:
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It is valuable to have a better understanding of factors that influence sea motion and to provide more accurate forecasts. In particular, we are motivated by data on wave heights and outgoing wave directions over a region in the Adriatic sea during the time of a storm, with the overarching goal of understanding the association between wave directions and wave heights to enable improved prediction of wave behavior. Our contribution is to develop a fully model-based approach to capture joint structured spatial and temporal dependence between a linear and an angular variable. Model fitting is carried out using a suitable data augmented Markov chain Monte Carlo (MCMC) algorithm. We illustrate with data outputs from a deterministic wave model for a region in the Adriatic Sea. The proposed joint model framework enables both spatial interpolation and temporal forecasting.
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Authors who are presenting talks have a * after their name.
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