Skip to main content

Smoothing spline Gaussian regression: more scalable computation via efficient approximation

Buy Article:

$51.00 plus tax (Refund Policy)

Summary. 

Smoothing splines via the penalized least squares method provide versatile and effective nonparametric models for regression with Gaussian responses. The computation of smoothing splines is generally of the order O(n3), n being the sample size, which severely limits its practical applicability. We study more scalable computation of smoothing spline regression via certain low dimensional approximations that are asymptotically as efficient. A simple algorithm is presented and the Bayes model that is associated with the approximations is derived, with the latter guiding the porting of Bayesian confidence intervals. The practical choice of the dimension of the approximating space is determined through simulation studies, and empirical comparisons of the approximations with the exact solution are presented. Also evaluated is a simple modification of the generalized cross-validation method for smoothing parameter selection, which to a large extent fixes the occasional undersmoothing problem that is suffered by generalized cross-validation.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: Bayesian confidence interval; Computation; Generalized cross-validation; Penalized least squares

Document Type: Research Article

Affiliations: 1: Yale University, New Haven, USA. 2: Purdue University, West Lafayette, USA.

Publication date: 2004-04-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more