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A fast ’Monte-Carlo cross-validation’ procedure for large least squares problems with noisy data. Numerische Mathematik, 56:1–23, 1989. H. T. Heath and G. Wahba. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21:215–223, 1979. H. F. Van Loan. Matrix Computaions. , Johns Hopkins University Press, Baltimore, 1996. J. W. Silverman. Nonparametric regression and generalized linear models - a roughness penalty approach. Chapman & Hill, London, 1994. C. Hansen.

All experimental work was done by the student. Even if most of the work is done jointly we would like to mention that Section 3 of Paper II is mostly the work of the student. 18 “lic” — 2007/4/23 — 11:29 — page 19 — #31 References [1] O. Axelsson. Solution of linear systems of equations: iterative methods. A. Barker, editor, Sparse Matrix Techniques. Springer Verlag, Berlin, Heidelberg, New York, 1976. [2] O. Axelsson. Iterative Solution Methods. Cambridge University Press, Cambridge, 1994. [3] Å.

Stability of conjugate gradient and Lanczos methods for linear least squares problems. SIAM Journal on Matrix Analysis and Applications, 19:720–736, 1998. [5] Å. Björck. The calculation of linear least squares problems. Acta Numerica, 13:1–53, 2004. A. Girard. A fast ’Monte-Carlo cross-validation’ procedure for large least squares problems with noisy data. Numerische Mathematik, 56:1–23, 1989. H. T. Heath and G. Wahba. Generalized cross-validation as a method for choosing a good ridge parameter.

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