Regularization methods for additive models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2810, Issue , 2003, Pages 509-520

  Avalos, Marta ^a   Grandvalet, Yves ^a   Ambroise, Christophe ^a  

^a UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS;

FORWARD SELECTION; HIGH DIMENSIONS; MODEL COMPLEXITY; MODEL SELECTION; REGULARIZATION METHODS; SIMULATION STUDIES; SMOOTHING PARAMETER; VARIABLE SELECTION;

ARTIFICIAL INTELLIGENCE;

EID: 32644477295     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-45231-7_47     Document Type: Article

References (17)

1. S. Bakin. Adaptive Regression and Model Selection in Data Mining Problems. PhD thesis, School of Mathematical Sciences, The Australian National University, Canberra, 1999.
2. L. Breiman. Heuristics of instability and stabilization in model selection. The Annals of Statistics, 24(6):2350-2383, 1996.
3. B. A. Brumback, D. Ruppert, and M. P. Wand. Comment on "Variable selection and function estimation in additive nonparametric regression using a data-based prior" by T. S. Shively, R. Khon and S. Wood. Journal of the American Statistical Association, 94(447):794-797, 1999.
4. E. Cantoni and T. J. Hastie. Degrees of freedom tests for smoothing splines. Biometrika, 89:251-263, 2002.
5. Z. Chen. Fitting multivariate regression functions by interaction spline models. J. R. Statist. Soc. B, 55(2):473-491, 1993.
6. Y. Grandvalet. Least absolute shrinkage is equivalent to quadratic penalization. In L. Niklasson, M. Bodén, and T. Ziemske, editors, ICANN'98, volume 1 of Perspectives in Neural Computing, pages 201-206. Springer, 1998.
7. Y. Grandvalet and S. Canu. Outcomes of the equivalence of adaptive ridge with least absolute shrinkage. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 445-451. MIT Press, 1998.
8. C. Gu and G. Wahba. Minimizing gcv/gml scores with multiple smoothing parameters via the newton method. SIAM J. Sci. Statist. Comput., 12:383-398, 1991.
9. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, Special Issue on Variable/Feature Selection, 3:1157-1182, 2003.
10. T. J. Hastie and R. J. Tibshirani. Generalized Additive Models, volume 43 of Monographs on Statistics and Applied Probability. Chapman & Hall, 1990.
11. J. D. Opsomer and D. Ruppert. A fully automated bandwidth selection method for fitting additive models. J. Multivariate Analysis, 73:166-179, 1998.
12. M.R. Osborne, B. Presnell, and B.A. Turlach. On the lasso and its dual. Journal of Computational and Graphical Statistics, 9(2):319-337, 2000.
13. T. S. Shively, R. Khon, and S. Wood. Variable selection and function estimation in additive nonparametric regression using a data-based prior. Journal of the American Statistical Association, 94(447):777-806, 1999.
14. R. J. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, B, 58(1):267-288, 1995.
15. R. J. Tibshirani, and K. Knight. The covariance inflation criterion for adaptive model selection. Journal of the Royal Statistical Society, B, 61(3):529-546, 1999.
16. S. N. Wood. Modelling and smoothing parameter estimation with multiple quadratic penalties. J. R. Statist. Soc. B, 62(2):413-428, 2000.
17. J. Ye. On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association, 93:120-131, 1998.