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题名: Geostatistical Model Averaging Based on Conditional Information Criteria
作者: Chen, Chun-Shu;Huang, Hsin-Cheng
贡献者: 統計資訊研究所
关键词: Conditional Akaike information criterion;Data perturbation;Spatial prediction;Stabilization;Stein’s unbiased risk estimate;Variable selectio
日期: 2011
上传时间: 2012-09-10T04:39:20Z
出版者: SpringLink
摘要: 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.
關聯: Environmental and Ecological Statistics, 19(1): 23-35
显示于类别:[統計資訊研究所] 期刊論文

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