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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/14057

Title: An Adaptive Selection Criterion for Spline Smoothing
Authors: Chen, Chun-Shu;Huang, H. C.
Contributors: 統計資訊研究所
Keywords: Effective degrees of freedom;Generalized degrees of freedom;Nonlinear estimate;Selection variability;Stein’s unbiased risk estimate
Date: 2009
Issue Date: 2012-09-10T04:40:47Z
Publisher: 國立中山大學數學系
Abstract: In nonparametric regression, smoothing splines are a popular method for curve fitting, in which selection of the smoothing parameter is crucial. In the past, there are many scholars who proposed various criteria for selecting the smoothing parameter, such as Mallows’ Cp, generalized maximum likelihood (GML), and the extended exponential (EE) criterion. Although Cp has been shown to be asymptotically optimal under the squared error loss (Li 1986), Kou and Efron (2002) utilized a geometric approach to show that Cp has a higher variability than GML and EE for small to moderately large sample sizes. On the other hand, GML and EE are more stable than Cp, but they do not possess the same asymptotic optimality as Cp. Therefore, an adaptive selection criterion would be proposed which is superior and more stable than Cp for small to moderately large sample sizes, and possesses the same asymptotic optimality as Cp.
Relation: The 18th Southern Area Statistical Conference
Appears in Collections:[統計資訊研究所] 會議論文

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