1 |
Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
|
2 |
Vapnik, V. N. (1998). Statistical learning theory, John Wiley, New York.
|
3 |
Wang, L. (Ed.) (2005). Support vector machines: Theory and application, Springer, New York.
|
4 |
Wang, Y., Wang, S. and Lai, K. (2011). Measuring financial risk with generalized asymmetric least squares regression. Applied Soft Computing, 11, 5793-5800.
DOI
ScienceOn
|
5 |
Yu, K., Lu, Z. and Stander, J. (2003). Quantile regression: Applications and current research area. The Statistician, 52, 331-350.
|
6 |
Hwang, C. (2010). M-quantile regression using kernel machine technique. Journal of the Korean Data & Information Science Society, 21, 973-981.
과학기술학회마을
|
7 |
Koenker, R. and Bassett. G. (1978). Regression quantile. Econometrica, 46, 33-50.
DOI
ScienceOn
|
8 |
Koenker, R. and Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 40, 122-142.
|
9 |
Kuhn, H. and Tucker, A. (1951). Nonlinear programming. In Proceedings of 2nd Berlekey Symposium on Mathematical Statistics and Probabilistics, University of California Press, CA, 481-492.
|
10 |
Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society A, 209, 415-446.
DOI
ScienceOn
|
11 |
Newey, W. K. and Powell, J. L. (1987). Asymmetric least squares estimation and testing. Econometrica, 55, 819-847.
DOI
ScienceOn
|
12 |
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.
|
13 |
Platt, J. (1999). Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods-Support Vector Learning, edited by B. Scholkopf, C. J. C. Burges and A. J. Smola, MIT Press, Cambridge, MA, 185-208.
|
14 |
Shim, J. and Hwang, C. (2013). Expected shortfall estimation using kernel machines. Journal of the Korean Data & Information Science Society, 24, 625-636.
과학기술학회마을
DOI
ScienceOn
|
15 |
Smola, A. and Scholkopf, B. (1998). On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22, 211-231.
DOI
|
16 |
Cole, T. J. and Green, P. J. (1992). Smoothing reference centile curves: The LMS method and penalized likelihood. Statistics in Medicine, 11, 1305-1319.
DOI
ScienceOn
|
17 |
Craven, P. andWahba, G. (1979). Smoothing noisy data with spline functions : Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerical Mathematics, 31, 377-403.
|
18 |
Flake, G. W. and Lawrence, S. (2002). Ecient SVM regression training with SMO. Machine Learning, 46, 271-290.
DOI
|
19 |
Schnabel, S. K. and Eilers, P. H. C. (2009). Optimal expectile smoothing. Computational Statistics & Data Analysis, 53, 4168-4177.
DOI
ScienceOn
|