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Activity Number: 264
Type: Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #317416
Title: Modeling Atlantic Ocean Salinity and Temperature Using a Multivariate Bayesian Functional Generalized Additive Model
Author(s): Christopher Krut* and Montserrat Fuentes and Brian J. Reich and Frederick Bingham
Companies: North Carolina State University and North Carolina State University and North Carolina State University and The University of North Carolina at Wilmington
Keywords: Bayesian Nonparametrics ; functional data ; spatial statistics ; spatio-temporal ; Ocean Salinity ; Ocean Temperature
Abstract:

Understanding climate change is widely recognized as a complicated and important problem. Studying changes in ocean salinity and temperature may help improve our understanding of this complex process. The following work considers the problem of predicting salinity and temperature depth profiles in the Atlantic Ocean using satellite and buoy measurements. The salinity and temperature depth profiles obtained from buoy data are treated as a bivariate functional response observed over space and time. Satellite measurements of salinity and temperature can also be viewed as space-time processes. A Bayesian Non-parametric approach is proposed for modeling these data. The method allows for non-linear covariate effects which are non-stationary in time and space. A structured covariance function is constructed to exploit local dependence and reduce the computational burden. The resulting approach is one that is both flexible and computationally efficient.


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