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Abstract Details
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Activity Number:
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166
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 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 - #305661 |
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Title:
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Local Spatial-Predictor Selection
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Author(s):
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Jonathan Bradley*+ and Noel Cressie and Tao Shi
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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 Avenue, Columbus, OH, 43210, United States
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Keywords:
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information criteria ;
generalized degrees of freedom ;
best linear unbiased predictor ;
model averaging ;
model combination
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Abstract:
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Consider the problem of spatial prediction of a random process from a spatial dataset. Global spatial-predictor selection provides a way to choose a single spatial predictor from a number of competing predictors. Instead, we consider local spatial-predictor selection at each spatial location in the domain of interest. This results in a hybrid predictor that could be considered global, since it takes the form of a combination of local predictors; we call this the locally selected spatial predictor. We pursue this idea here using the (empirical) deviance information as our criterion for (global and local) predictor selection. In a small simulation study, the relative performance of this combined predictor, relative to the individual predictors, is assessed.
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