Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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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)
  • Received : 20101100
  • Accepted : 20110100
  • Published : 2011.03.31
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Abstract

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.

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References

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