Approximating data with weighted smoothing splines
P.L. Davies and
M. Meise
Journal of Nonparametric Statistics, 2008, vol. 20, issue 3, 207-228
Abstract:
Given a data set (ti, yi), i=1, ˙s, n with ti∈[0, 1] non-parametric regression is concerned with the problem of specifying a suitable function fn:[0, 1]→ℝ such that the data can be reasonably approximated by the points (ti, fn(ti)), i=1, ˙s, n. If a data set exhibits large variations in local behaviour, for example large peaks as in spectroscopy data, then the method must be able to adapt to the local changes in smoothness. Whilst many methods are able to accomplish this, they are less successful at adapting derivatives. In this paper we showed how the goal of local adaptivity of the function and its first and second derivatives can be attained in a simple manner using weighted smoothing splines. A residual-based concept of approximation is used which forces local adaptivity of the regression function together with a global regularization which makes the function as smooth as possible subject to the approximation constraints.
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/10485250801948625 (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:taf:gnstxx:v:20:y:2008:i:3:p:207-228
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485250801948625
Access Statistics for this article
Journal of Nonparametric Statistics is currently edited by Jun Shao
More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().