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题名: 空間迴歸模型變數選取方法(1/2)
Variable Selection for Spatial Regression Models
作者: 陳春樹
贡献者: 統計資訊研究所
关键词: Conditional information criterion;Data perturbation;Spatial prediction;Stabilization;Stein’s unbiased risk estimate;Variable selection.
日期: 2009
上传时间: 2012-09-10T04:40:17Z
出版者: 行政院國家科學委員會
摘要: Variable selection in geostatistical regression is an important problem, but has not been well
studied in the literature. In the first year of this project, 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. Then Stein’s unbiased
risk estimate is applied to select the penalty parameter, which takes estimation of model
parameters into account and can be computed using a simple Monte Carlo method. In the
second year of this project, we will continue to complete the expected goals.
關聯: 國科會計畫, 計畫編號: NSC98-2118-M018-003-MY2; 研究期間: 9808-9907
显示于类别:[統計資訊研究所] 國科會計畫

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