This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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136
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract - #306952 |
Title:
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Efficient Kriging for Large Spatial Fields
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Author(s):
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Karl Pazdernik*+ and Ranjan Maitra and Douglas Nychka and Stephan Sain
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Companies:
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Iowa State University and Iowa State University and National Center for Atmospheric Research and National Center for Atmospheric Research
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Address:
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411 WELCH AVE, Ames, IA, 50014,
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Keywords:
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spatial ;
Kriging ;
gaussian random field ;
prediction ;
sparse matrix ;
efficient
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Abstract:
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In spatial statistics, a common method for prediction over a Gaussian Random Field (GRF) is Kriging. Unfortunately, Kriging requires inverting a covariance matrix which, depending on the data set, can be extremely large and thus computationally intensive. Thus, I propose a new approach to estimation and prediction that uses a combination of concepts from reduced-rank Kriging, Ridge Regression, and sparse matrix methodology. I will contrast the gains in run time versus the loss in precision, as well as explore the connection between the actual parameter values and the choice of basis functions in the model. This method is applied to a temperature data set of the Midwestern U.S. with very good results.
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