Communications for Statistical Applications and Methods

  • 제18권2호
  • /
  • Pages.165-170
  • /
  • 2011
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

Support Vector Quantile Regression Using Asymmetric e-Insensitive Loss Function

  • Shim, Joo-Yong (Department of Data Science, Inje University) ;
  • Seok, Kyung-Ha (Department of Data Science and Institute of Statistical Information, Inje University) ;
  • Hwang, Chang-Ha (Department of Statistics, Dankook University) ;
  • Cho, Dae-Hyeon (Department of Data Science and Institute of Statistical Information, Inje University)
  • 투고 : 20101100
  • 심사 : 20110100
  • 발행 : 2011.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Support vector quantile regression(SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a limitation of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide sparsity. The experimental results are presented to illustrate the performance of the proposed method by comparing it with nonsparse SVQR.

키워드

참고문헌

  1. Koenker, R. (2005). Quantile Regression, Cambridge University Press, Cambridge.
  2. Koenker, R. and Bassett, G. (1978). Regression quantile, Econometrica, 46, 33–50.
  3. Kuhn, H. W. and Tucker, A. W. (1951). Nonlinear programming, In Proceedings of 2nd Berkeley Symposium, Berkeley: University of California Press, 481–492.
  4. Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society, A:415-446. https://doi.org/10.1098/rsta.1909.0016
  5. Smola, A. and Scholkopf, B. (1998). On a Kernel-based method for pattern recognition, regression, approximation and operator inversion, Algorithmica, 22, 211–231.
  6. Takeuchi, I., Le, Q. V., Sears, T. D. and Smola, A. J. (2006). Nonparametric quantile estimation, Journal of Machine Learning Research, 7, 1231–1264.
  7. Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine, Journal of Machine Learning Research, 1, 211–244.
  8. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory, Springer, New York.
  9. Vapnik, V. N. (1998). Statistical Learning Theory, John Wiley, New York.
  10. Wang, L.(Ed.) (2005). Support Vector Machines: Theory and Application, Springer, New York.
  11. Yu, K., Lu, Z. and Stander, J. (2003). Quantile regression: Applications and current research area, Journal of the Royal Statistical Society, Series D(The Statistician), 52, 331–350.