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Activity Number: 164
Type: Topic Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #307570
Title: Stochastic Downscaling for Large Spatial Data Sets
Author(s): William Kleiber*+
Companies: University of Colorado
Keywords: Downscaling ; Kriging ; Large spatial datasets ; Gaussian process
Abstract:

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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