We use a reproducing kernel Hilbert space representation to derive the smoothing spline solution when the smoothness penalty is a function λ(t) of the design space t, thereby allowing the model to adapt to various degrees of smoothness in the structure of the data. We propose a convenient form for the smoothness penalty function and discuss computational algorithms for automatic curve fitting using a generalised crossvalidation measure. © 2006 Biometrika Trust.

Original publication

DOI

10.1093/biomet/93.1.113

Type

Journal article

Journal

Biometrika

Publication Date

01/03/2006

Volume

93

Pages

113 - 125