Some characteristics on the selection of spline smoothing parameter

Chun Shu Chen, Yi Tsz Huang

Research output: Contribution to journalArticlepeer-review


The smoothing spline method is used to fit a curve to a noisy data set, where selection of the smoothing parameter is essential. An adaptive Cp criterion (Chen and Huang 2011) based on the Stein’s unbiased risk estimate has been proposed to select the smoothing parameter, which not only considers the usual effective degrees of freedom but also takes into account the selection variability. The resulting fitted curve has been shown to be superior and more stable than commonly used selection criteria and possesses the same asymptotic optimality as Cp. In this paper, we further discuss some characteristics on the selection of smoothing parameter, especially for the selection variability.

Original languageEnglish
Pages (from-to)1307-1317
Number of pages11
JournalCommunications in Statistics - Theory and Methods
Issue number6
StatePublished - 19 Mar 2018


  • Non parametric regression
  • selection variability
  • smoothing spline
  • Stein’s unbiased risk estimate


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