Algorithms for a partially regularized least squares problem by Ingegerd Skoglund.

By Ingegerd Skoglund.

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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.

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. 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.

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. Rank-Deficient and Discrete Ill-Posed Problems. Numerical Aspects of Linear Inversion. SIAM, Philadelphia, 1998.

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