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G. Wahba 495Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer,Berlin.Wahba, G. (1977). Practical approximate solutions to linear operator equationswhen the data are noisy. SIAM Journal of Numerical Analysis,14:651–667.Wahba, G. (1983). Bayesian “confidence intervals” for the cross-validatedsmoothing spline. Journal of the Royal Statistical Society, Series B,45:133–150.Wahba, G. (1990). Spline Models for Observational Data.SIAM.CBMS–NSFRegional Conference Series in Applied Mathematics, vol. 59.Wahba, G., Johnson, D.R., Gao, F., and Gong, J. (1995). Adaptive tuning ofnumerical weather prediction models: Randomized GCV in three and fourdimensional data assimilation. Monthly Weather Review, 123:3358–3369.Wahba, G. and Wold, S. (1975). A completely automatic French curve. Communicationsin Statistics, 4:1–17.Wang, Y. (2011). Smoothing Splines: Methods and Applications. Chapman&Hall,London.Wright, S.J. (2012). Accelerated block-coordinate relaxation for regularizedoptimization. SIAM Journal of Optimization, 22:159–186.Preprintandsoftware available at http://pages.cs.wisc.edu/~swright/LPS/.Yuan, M. and Lin, Y. (2006). Model selection and estimation in regressionwith grouped variables. Journal of the Royal Statistical Society, Series B,68:49–67.

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