Optimising the degree of data smoothing for locally adaptive finite element bivariate smoothing splines
نویسندگان
چکیده
منابع مشابه
Locally adaptive smoothing method based on B-splines
This paper presents a novel method for the edge-preserving smoothing of biomedical images. It is based on the convolution of the image with scaled B-splines. The size of the spline convolution kernel at each image position is adaptive and matched to the underlying image characteristics; i.e., wide splines for smooth regions and narrow ones for pixels belonging to edges. Consequently, the algori...
متن کاملApproximating data with weighted smoothing splines∗
Given a data set (ti, yi), i = 1, . . . , n with the 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, . . . , n. If a data set exhibits large variations in local behaviour, for example large peaks as in spectroscopy data, then the method must ...
متن کاملEquivalent Kernels for Smoothing Splines
In the study of smoothing spline estimators, some convolution-kernellike properties of the Green’s function for an appropriate boundary value problem, depending on the design density, are needed. For the uniform density, the Green’s function can be computed more or less explicitly. Then, integral equation methods are brought to bear to establish the kernel-like properties of said Green’s functi...
متن کاملSmoothing with Cubic Splines
A spline function is a curve constructed from polynomial segments that are subject to conditions or continuity at their joints. In this paper, we shall present the algorithm of the cubic smoothing spline and we shall justify its use in estimating trends in time series. Considerable effort has been devoted over several decades to developing the mathematics of spline functions. Much of the intere...
متن کاملSmoothing parameter selection for smoothing splines: a simulation study
Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so{called risk estimation methods. To the best of the author's knowledge, the empirical performances of these two risk...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ANZIAM Journal
سال: 2000
ISSN: 1445-8810
DOI: 10.21914/anziamj.v42i0.621