Spatially adaptive smoothing splines

Alexandre Pintore, Paul Speckman and Chris C. Holmes

Biometrika, 2006, vol. 93, issue 1, 113-125

Abstract: 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. Copyright 2006, Oxford University Press.

Date: 2006
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/93.1.113 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:93:y:2006:i:1:p:113-125

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().