Skip to main content
Log in

Smoothing noisy data with spline functions

  • Published:
Numerische Mathematik Aims and scope Submit manuscript

Summary

A procedure for calculating the trace of the influence matrix associated with a polynomial smoothing spline of degree2m−1 fitted ton distinct, not necessarily equally spaced or uniformly weighted, data points is presented. The procedure requires orderm 2 n operations and therefore permits efficient orderm 2 n calculation of statistics associated with a polynomial smoothing spline, including the generalized cross validation. The method is a significant improvement over an existing method which requires ordern 3 operations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Craven, P., Wahba, G.: Smoothing noisy data with spline functions. Numer. Math.31, 377–403 (1979)

    Google Scholar 

  2. Curry, H.B., Schoenberg, I.J.: On polya frequency functions IV: The fundamental spline functions and their limits. J. Anal. Math.17, 71–107 (1966)

    Google Scholar 

  3. de Boor, C.: A Practical Guide to Splines. Appl. Math. Sci. vol. 27. New York: Springer 1978

    Google Scholar 

  4. Dongarra, J.J., Moler, C.B., Bunch, J.R., Stewart, G.W.: Linpack User's Guide. Philadelphia: Society for Industrial and Applied Mathematics 1979

    Google Scholar 

  5. Eldén, L.: An algorithm for the regularization of ill-conditioned banded least squares problems. SIAM J. Sci. Stat. Comput.5, 237–254 (1984)

    Google Scholar 

  6. Eldén, L.: A note on the computation of the generalized cross-validation function for illconditioned least squares problems. BIT24, 467–472 (1984)

    Google Scholar 

  7. Erisman, A.M., Tinney, W.F.: On computing certain elements of the inverse of a sparse matrix. Commun. ACM18, 177–179 (1975)

    Google Scholar 

  8. Golub, G.H., Plemmons, R.J.: Large-scale geodetic least-squares adjustment by dissection and orthogonal decomposition. Lin. Alg. Appl.34, 3–27 (1980)

    Google Scholar 

  9. Haley, S.B.: Solution of band matrix equations by projection-recurrence. Lin. Alg. Appl.32, 33–48 (1980)

    Google Scholar 

  10. IMSL: Library Reference Manual, edition 9. Houston: IMSL 1982

    Google Scholar 

  11. Reinsch, C.H.: Smoothing by spline functions. Numer. Math.10, 177–183 (1967)

    Google Scholar 

  12. Reinsch, C.H.: Smoothing by spline functions, II. Numer. Math.16, 451–454 (1971)

    Google Scholar 

  13. Schoenberg, I.J.: Spline functions and the problem of graduation. Proc. Natl. Acad. Sci. USA52, 947–950 (1964)

    Google Scholar 

  14. Takahashi, K., Fagan, J., Chin, M.-S.: Formation of a sparse bus impedance matrix and its application to short circuit study. Power Industry Computer Applications Conf. Proc. Minneapolis, Minn.8, 63–69 (June 4–6, 1973)

    Google Scholar 

  15. Utreras, F.: Sur le choix de parametre d'adjustement dans le lissage par fonctions spline. Numer. Math.34, 15–28 (1980)

    Google Scholar 

  16. Utreras, F.: Optimal smoothing of noisy data using spline functions. SIAM J. Sci. Stat. Comput.2, 349–362 (1981)

    Google Scholar 

  17. Utreras, F.: Natural spline functions, their associated eigenvalue problem. Numer. Math.42, 107–117 (1983)

    Google Scholar 

  18. Wahba, G.: Smoothing noisy data with spline functions. Numer. Math.24, 383–392 (1975)

    Google Scholar 

  19. Wahba, G.: Bayesian “confidence intervals” for the cross-validated smoothing spline. J.R. Stat. Soc., Ser. B45, 133–150 (1983)

    Google Scholar 

  20. Wecker, W.E., Ansley, C.F.: The signal extraction approach to non linear regression and spline smoothing. J. Am. Stat. Assoc.78, 81–89 (1983)

    Google Scholar 

  21. Weinert, H.L., Byrd, R.H., Sidhu, G.S.: A stochastic framework for recursive computation of spline functions: Part II, smoothing splines. J. Optimization Theory Appl.30, 255–268 (1980)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hutchinson, M.F., de Hoog, F.R. Smoothing noisy data with spline functions. Numer. Math. 47, 99–106 (1985). https://doi.org/10.1007/BF01389878

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01389878

Subject Classifications

Navigation