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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/14051

Title: Geostatistical Model Averaging Based on Conditional Information Criteria
Authors: Chen, Chun-Shu;Huang, Hsin-Cheng
Contributors: 統計資訊研究所
Keywords: Conditional Akaike information criterion;Data perturbation;Spatial prediction;Stabilization;Stein’s unbiased risk estimate;Variable selectio
Date: 2011
Issue Date: 2012-09-10T04:39:20Z
Publisher: SpringLink
Abstract: Variable selection in geostatistical regression is an important problem,
but has not been well studied in the literature. In this paper, we focus on spatial prediction
and consider a class of conditional information criteria indexed by a penalty
parameter. Instead of applying a fixed criterion, which leads to an unstable predictor
in the sense that it is discontinuous with respect to the response variables due to that
a small change in the response may cause a different model to be selected, we further
stabilize the predictor by local model averaging, resulting in a predictor that is not
only continuous but also differentiable even after plugging-in estimated model parameters.
Then Stein’s unbiased risk estimate is applied to select the penalty parameter,
leading to a data-dependent penalty that is adaptive to the underlying model. Some
numerical experiments showsuperiority of the proposed model averaging method over
some commonly used variable selection methods. In addition, the proposed method
is applied to a mercury data set for lakes in Maine.
Relation: Environmental and Ecological Statistics, 19(1): 23-35
Appears in Collections:[統計資訊研究所] 期刊論文

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