Abstract Details
Activity Number:
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164
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 5, 2013 : 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 - #307570 |
Title:
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Stochastic Downscaling for Large Spatial Data Sets
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Author(s):
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William Kleiber*+
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Companies:
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University of Colorado
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Keywords:
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Downscaling ;
Kriging ;
Large spatial datasets ;
Gaussian process
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Abstract:
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We discuss methods for surface estimation over heterogeneous domains in the presence of large and complex spatial data. Traditional approaches are infeasible due to size of the covariance matrices that must be manipulated. We develop an approximation to kriging, borrowing ideas from the spline literature that are especially useful for surface estimation in the context of large sample sizes. We suggest estimation techniques that do not directly rely on the likelihood, either via cross validation or generalized cross validation. Finally, the method is illustrated on simulated and actual datasets.
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Authors who are presenting talks have a * after their name.
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