This preliminary investigation deals with the use of spline functions for smoothing spectral data. Three variant implementations are outlined and some tentative results are presented.doi:10.1016/0368-2048(90)87081-XA LOSEVElsevier B.V.Journal of Electron Spectroscopy & Related Phenomena...
J.: On interpolation by spline functions and its minimal properties, p. 109. On Approximation Theory. Proceedings of the Conference held in the Mathematical Research Institute at Oberwolfach, Black Forest, August 4–10, 1963. Basel-Stuttgart: Birkhäuser 1964. Google Scholar Funk, P.: ...
Smoothing by spline functions: applications in electron spectroscopy This preliminary investigation deals with the use of spline functions for smoothing spectral data. Three variant implementations are outlined and some tent... A Losev - 《Journal of Electron Spectroscopy & Related Phenomena》 被引量: ...
C. H. Reinsch,Smoothing by spline functions, Numer. Mathem.16 (1971), 451–454. Article MathSciNet Google Scholar I. Galligani,Sulla regolarizzazione dei dati sperimentali, Calcolo8 (1972), 359–376. Article MATH MathSciNet Google Scholar I. J. Schoenberg,Spline functions and the pr...
For an alternative to'smoothingspline', you can use thecsapscubic smoothing spline function or other spline functions that allow greater control over what you can create. SeeIntroducing Spline Fitting. Compare Cubic and Smoothing Spline Fit Using Curve Fitter ...
Smoothing is a method of reducing the noise within a data set. Curve Fitting Toolbox™ allows you to smooth data using methods such as moving average, Savitzky-Golay filter and Lowess models or by fitting a smoothing spline. Smooth data interactively using theCurve Fitterapp or at the comman...
rive the basic spline functions. In Section III we show that by a change of basis we can develop numerically sound algorithms for the calculation of these splines. In Section IVwe then prove a convergence result for smoothing splines using results from the ...
Secondly, its global smoothing parameter can only provide constant amount of smoothing, which often results in poor performances when estimating inhomogeneous functions. In this work, we introduce a class of adaptive smoothing spline models that is derived by solving certain stochastic differential ...
Both explicit and parametric cubic spline functions are used in a least-squares algorithm to obtain a least-squares cubic polynomial spline fit for smoothing data. The primary advantage of the least-squares spline technique is that a set of data can be represented by a continuous function with ...
C. H. Reinsch, Smoothing by Spline Functions. II, Numer. Math., 16 (1971), pp. 451–454. CrossRef A. Savitzky AND M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Analytical Chemistry, 36 (1964), pp. 1627–1639. CrossRef J. Steinier...