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Details for article 11 of 23 found articles
| Title: |
Fitting noisy data using cross-validated cubic smoothing splines |
| Author: |
Pope, S. B. Gadh, R. |
| Appeared in: |
Communications in statistics |
| Paging: | Volume 17 (1988) nr. 2 pages 349-376 |
| Year: |
1988 |
| Contents: |
An algorithm is described for approximating an unknown function f(x), given many function values containing random noise. The approximation constructed is a cubic spline g(x) with sufficient basis functions to represent f(x) accurately. The basis-function coefficients are determined by minimizing a combination of the infidelity E (the mean-square errorz between g(x) and the data,and the roughness T (which is a measure of the tortuosity of g(x)). The quantity minimized is E+pT, where p is a smoothing parameter. A suitable value of p is determined by cross validation.Results of numerical tests are reported which show that this algorithm is superior to least-squares cubic splines: in general the statistical errors are substantially less, and they are insensitive to the number of basis functions used. |
| Publisher: |
Taylor & Francis |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 11 of 23 found articles
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