References
- Cawley, G. C., Talbot, N. L. C., Foxall, R. J., Dorling, S. R. and Mandic, D. P. (2004). Heteroscedastic Kernel ridge regressing, Neurocomputing, 57, 105-124.
- Craven, P. and Wahba, G. (1979). Smoothing noisy data with spline functions: Estimation the correct degree of smoothing by the moethod of generalized cross-validation, Numerical Mathematics, 31, 377-403. https://doi.org/10.1007/BF01404567
- Flake, G. W. and Lawrence, S. (2002). Effcient SVM regression traning with SMO, Machine Learning, 46, 271-290. https://doi.org/10.1023/A:1012474916001
- Hardle, W. (1989). Applied Nonparametric Regression, Cambridge University Press, Cambridge.
- Kim, Y., Shim, J., Lee, J. T. and Hwang, C. (2009). Combination of value-at-risk models with support vector machine, Communications of the Korean Statistical Society, 16, 791-801. https://doi.org/10.5351/CKSS.2009.16.5.791
- Kimeldorf, G. S. and Wahba, G. (1971). Some results on Tchebycheffian spline functions, Journal of Mathematical Analysis and its Applications, 3, 82-95.
- Koenker, R. and Bassett, G. (1978). Regression quantile, Econometricak, 46, 33-50. https://doi.org/10.2307/1913643
- Koenker, R. and Hallock, K. F. (2001). Quantile regression, Journal of Economic Perspectives, 40, 122-142.
- Koenker, R. and Park, B. J. (1996). An interior point algorithm for nonlinear quantile regression, Journal of Econometrics, 71, 265-283. https://doi.org/10.1016/0304-4076(96)84507-6
- Perez-Cruz, F., Navia-Vazquez, A., Alarcon-Diana, P. L. and Artes-Rodriguez, A. (2000). An IRWLS procedure for SVR, In Proceedings of European Association for Signal Processing, EUSIPO 2000, Tampere, Finland.
- Pantt, J. (1998). Sequentail Minimal Optimization: A Fast Algorithm for Training Supprot Vector Machines, Microsoft Research Technical Report MSR-TR-98-14.
- Smola, A. J. and Scholkopf, B. (1998). On a Kernel-based method for pattern recognition, regression, Approximation and Operator Inversion Algorithmica, 22, 211-231.
- Takeuchi, I., Le, Q. V., Sears, T. D. and Smola, A. J. (2006). Nonparametric quantile estimation, Journal of Machine Learning Research, 7, 1231-1264.
- Vapnik, V. N. (1995). The Nature of Statistical Learning Theory, Springer, New York.
- Vapnik, V. N. (1998). Statistical Learning Theory, John Wiley, New York.
- Wang, L. (2005). Support Vector Machines: Theory and Application, Springer, Berlin Heidelberg, New York.
- Yu, K., Lu, Z. and Stander, J. (2003). Quantile regression: Applications and current research area, The Statistician, 52, 331-350.
- Yuan, M. (2006). GACV for quantile smoothing splines, Computational Statistics and Data Analysis, 50, 813-829. https://doi.org/10.1016/j.csda.2004.10.008
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- Weighted quantile regression via support vector machine vol.42, pp.13, 2015, https://doi.org/10.1016/j.eswa.2015.03.003