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Activity Number:
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61
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305501 |
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Title:
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Kernel-Based Models for Space-Time Data
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Author(s):
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Catherine Calder*+ and Tao Shi and Candace Berrett
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Companies:
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The Ohio State University and The Ohio State University and The Ohio State University
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Address:
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1958 Neil Ave., Columbus, OH, 43210,
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Keywords:
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spatial statistics ; Bayesian modeling ; MCMC ; nonstationarity ; aerosols
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Abstract:
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Kernel-based models for space-time data offer a flexible and descriptive framework for studying atmospheric processes. Nonstationary and anisotropic covariance structures can be readily accommodated by allowing kernel parameters to vary over space and time. In addition, dimension reduction strategies make model fitting computationally feasible for large data sets. We illustrate the use of one version of these models in a study of carbonaceous aerosols over mainland Southeast Asia. In addition, efficient MCMC strategies are introduced to further facilitate model fitting and comparison.
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