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Extra info for Algorithms for a partially regularized least squares problem
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  O. Axelsson. Solution of linear systems of equations: iterative methods. A. Barker, editor, Sparse Matrix Techniques. Springer Verlag, Berlin, Heidelberg, New York, 1976.  O. Axelsson. Iterative Solution Methods. Cambridge University Press, Cambridge, 1994.  Å.
Stability of conjugate gradient and Lanczos methods for linear least squares problems. SIAM Journal on Matrix Analysis and Applications, 19:720–736, 1998.  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.