This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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183
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 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 - #307324 |
Title:
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Geostatistical Model Averaging
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Author(s):
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Chun-Shu Chen*+ and Hsin-Cheng Huang
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Companies:
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National Changhua University of Education and Academia Sinica
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Address:
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, , 50007, Taiwan
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Keywords:
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Akiake information criterion ;
conditional information criterion ;
data perturbation ;
spatial prediction ;
stabilization ;
variable selection
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Abstract:
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Variable selection in geostatistical regression is an important problem, but has not been well studied in the literature. In this talk, we focus on spatial prediction and consider a class of conditional information criteria indexed by a penalty parameter. Instead of directly applying a criterion, which leads to a discontinuous spatial predictor with respect to the response variables, we further stabilize the predictor by local model averaging, resulting in a predictor that is not only continuous but also differentiable. A version of Stein's unbiased risk estimate is applied to select the penalty parameter. It takes estimation of model parameters into account and can be computed using a simple Monte Carlo method. The superiority of the proposed model averaging method is shown by some numerical experiments.
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