Non-crossing quantile regressions by SVM

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 1, Issue , 2004, Pages 401-406

  Takeuchi, Ichiro ^a   Furuhashi, Takeshi ^b  

^a MIE UNIVERSITY   (Japan)

^b NAGOYA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; LINEAR PROGRAMMING; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; QUADRATIC PROGRAMMING;

HETEROSCEDASTICITY; NON-CROSSING QUANTILE REGRESSION; QUANTILE FUNCTIONS; SUPPORT VECTORS (SV);

REGRESSION ANALYSIS;

EID: 10944229030     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. R. Koenker and G. Bassett. Regression quantiles, econometrica, 46(1):33-50, 1978.
2. I. Barrondale and F. D. K. Roberts. An improved algorithm for discrete 11 linear approximation. SIAM Journal on Numerical Analysis, 10(5):839-848, 1973.
3. 1 approximation. The Computer Journal, 14(2): 184-188, 1971.
4. P. Chaudhuri. Nonparametric estimates of regression quantiles and their local bahadur representation. The Annals of Statistics, 2(19):760-777, 1991.
5. X. He. Quantile curves without crossing. The American Statistician, 51(2):186-192, 1997.
6. B. E. Boser, I. M. Guyon, and V. N. Vapnik. a training algorithm for optimal margin classifiers. In proceedings of the 5th annual ACM workshop on computational Learning theory, pages 144-152. Pittsburg, PA: ACM Press, 1992.
7. V. Vapnik. The nature of Statistical Learning Theory. Springer-Verlag, 1995.
8. R. Vanderbei. Loqo: An interior point code for quadratic programming. Optimization Methods and Software, 12:451-484, 1999.
9. J. Platt. Fast training of support vector machines using sequential minimal optimization. Cambridge, 1998.
10. R. Koenker, P. Ng, and S. Portnoy. Quantile smoothing splines. Biometrika, 81:673-680, 1994.
11. G. W. Flake and S. Lawrence. Efficient svm regression training with smo. Machine Learning, 46:271-290, 2002.
12. S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, and K. R. K. Murthy. Improvements to the smo algorithm for svm regression. IEEE Trans. on Neural Networks, 11(5):1188-1193, 2000.
13. A. J. Smola and B. Scholkopf. A tutorial on support vector regression. Technical Report NC2-TR-1998-030, NeuroCOLT2, 1998.
14. B. Scholkopf and A. J. Smola. Learning with Kernels. The MIT Press, 2002.