This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 183
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #307324
Title: Geostatistical Model Averaging
Author(s): Chun-Shu Chen*+ and Hsin-Cheng Huang
Companies: National Changhua University of Education and Academia Sinica
Address: , , 50007, Taiwan
Keywords: Akiake information criterion ; conditional information criterion ; data perturbation ; spatial prediction ; stabilization ; variable selection
Abstract:

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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