Statistics – Methodology
Scientific paper
2007-12-11
Journal of Nonparametric Statistics, 20:3, (2008) 207-228
Statistics
Methodology
Scientific paper
Given a data set (t_i, y_i), i=1,..., n with the t_i in [0,1] non-parametric regression is concerned with the problem of specifying a suitable function f_n:[0,1] -> R such that the data can be reasonably approximated by the points (t_i, f_n(t_i)), i=1,..., 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 show 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.
Davies Patrick Laurie
Meise M.
No associations
LandOfFree
Approximating Data with weighted smoothing Splines does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Approximating Data with weighted smoothing Splines, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Approximating Data with weighted smoothing Splines will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-474309