Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T05:24:16.741Z Has data issue: false hasContentIssue false

On algorithms for generalised smoothing splines

Published online by Cambridge University Press:  17 February 2009

M. R. Osborne
Affiliation:
Department of Statistics, Research School of Social Sciences, Australian National University, G.P.O. Box 4, Canberra, A.C.T. 2601, Australia.
Tania Prvan
Affiliation:
Department of Pure and Applied Mathematics, Washington State University, Pullman, Washington 99164, U.S.A.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Recently, considerable interest has been shown in the connection between smoothing splines and a particular class of stochastic processes. Here the connection with an equivalent class of least squares problems is used to develop algorithms, and properties of the solution are examined. We give an estimate of the condition number of the solution process and compare this with an estimate for the condition number of the Reinsch algorithm in its conventional implementation.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 1988

References

[1]Anderson, B. D. O. and Moore, J. B., Optimal filtering (Prentice-Hall, Englewood Cliffs, 1979).Google Scholar
[2]Ansley, C. F. and Kohn, R., “Estimation, filtering and smoothing in state space models with incompletely specified initial conditions”, Ann. Statist. 13 (1985) 12861316.CrossRefGoogle Scholar
[3]Ansley, C. F. and Kohn, R., “On the equivalence of two stochastic approaches to spline smoothing”, J. Appl. Prob. Special volume 23A (1986) 391406.CrossRefGoogle Scholar
[4]Billingsley, P., Probability and measure (Wiley, 1979).Google Scholar
[5]de Boor, C., A practical guide to splines (Springer-Verlag, 1978).CrossRefGoogle Scholar
[6]de Hoog, F. and Hutchinson, M. F., “Two fast procedures for calculating smoothing splines”, Report CMA-R27-85, Centre for Mathematical Analysis, Australian National University, 1985.Google Scholar
[7]Duncan, D. B. and Horn, S. D., “Linear dynamic recursive estimation from the viewpoint of regression analysis”, J. Amer. Statist. Assoc. 67 (1972) 815821.Google Scholar
[8]Gerig, T. M. and Gallant, A. R., “Computing methods for linear models subject to linear constraints”, J. Statist. Comput. Simulation 3 (1975) 283296.CrossRefGoogle Scholar
[9]Golub, G. H. and Van Loan, C. F., Matrix computations (Johns Hopkins University Press, 1983).Google Scholar
[10]Harvey, A. C. and Phillips, G. D. A., “Maximum likelihood estimation of regression models with autoregressive-moving average disturbances”, Biometrika 66 (1979) 4958.Google Scholar
[11]Householder, A. S., Principles of numerical analysis (McGraw-Hill, 1953).Google Scholar
[12]Kohn, R. and Ansley, C. F., “On the smoothness properties of the best linear unbiased estimate of a stochastic process observed with noise”, Ann. Statist. 11 (1983), 10111017.CrossRefGoogle Scholar
[13]Kohn, R. and Ansley, C. F., “A new algorithm for spline smoothing based on smoothing a stochastic process”, Working Paper 85–006, Australian Graduate School of Management, 1985.Google Scholar
[14]Luenberger, D. G., Optimization by vector space methods (Wiley, 1969).Google Scholar
[15]Paige, C. C. and Saunders, M. A., “Least squares estimation of discrete linear dynamic systems using orthogonal transformations”, SIAM J. Numer. Anal. 14 (1977) 180193.CrossRefGoogle Scholar
[16]Pondit, S. M. and Wu, S. M., Time series and system analysis with application (Wiley, 1983).Google Scholar
[17]Reinsch, C. H., “Smoothing by spline functions”, Numer. Math. 10 (1967) 177183.CrossRefGoogle Scholar
[18]Silverman, B. W., “A fast and efficient cross-validation method for smoothing parameter choice in spline regression”, J. Amer. Statist. Assoc. 79 (1984) 584589.CrossRefGoogle Scholar
[19]Silverman, B. W., “Some aspects of the spline smoothing approach to nonparametric regression curve fitting”, J. Roy. Statist. Soc. Ser. B. 47 (1985) 152.Google Scholar
[20]Speckman, P., “Spline smoothing and optimal rates of convergence in nonparametric regression models”, Ann. Statist. 13 (1985) 970983.CrossRefGoogle Scholar
[21]Utreras, D. F., “Sur le choix du parametre d'adjustement dans le lissage par fonctions spline”, Numer. Math. 34 (1980) 1528.Google Scholar
[22]Wahba, G., “Improper priors, spline smoothing, and the problem of guarding against model errors in regression”, J. Roy. Statist. Soc. Ser. B. 40 (1978) 364372.Google Scholar
[23]Wahba, G., “A comparison of GCV and GML for choosing the smoothing parameter in the generalised spline smoothing problem”, Ann. Statist. 13 (1985) 13781402.CrossRefGoogle Scholar
[24]Wecker, W. and Ansley, C. F., “The signal extraction approach to nonlinear regression and spline smoothing”, J. Amer. Statist. Assoc. 78 (1983) 8189.CrossRefGoogle Scholar
[25]Weinert, H. L., Byrd, R. H. and Sidhu, C. S., “A stochastic framework for recursive computation of spline functions: Part II, smoothing splines”, J. Optim. Theory Appl. 30 (1980) 255268.CrossRefGoogle Scholar