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Composite support vector quantile regression estimation

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  • Jooyong Shim
  • Changha Hwang
  • Kyungha Seok

Abstract

In this paper we propose a new nonparametric regression method called composite support vector quantile regression (CSVQR) that combines the formulations of support vector regression and composite quantile regression. First the CSVQR using the quadratic programming (QP) is proposed and then the CSVQR utilizing the iteratively reweighted least squares (IRWLS) procedure is proposed to overcome weakness of the QP based method in terms of computation time. The IRWLS procedure based method enables us to derive a generalized cross validation (GCV) function that is easier and faster than the conventional cross validation function. The GCV function facilitates choosing the hyperparameters that affect the performance of the CSVQR and saving computation time. Numerical experiment results are presented to illustrate the performance of the proposed method Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Jooyong Shim & Changha Hwang & Kyungha Seok, 2014. "Composite support vector quantile regression estimation," Computational Statistics, Springer, vol. 29(6), pages 1651-1665, December.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:6:p:1651-1665
    DOI: 10.1007/s00180-014-0511-4
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    References listed on IDEAS

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