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Abstract Details

Activity Number: 166
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #305661
Title: Local Spatial-Predictor Selection
Author(s): Jonathan Bradley*+ and Noel Cressie and Tao Shi
Companies: The Ohio State University and The Ohio State University and The Ohio State University
Address: 1958 Neil Avenue, Columbus, OH, 43210, United States
Keywords: information criteria ; generalized degrees of freedom ; best linear unbiased predictor ; model averaging ; model combination
Abstract:

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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