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Composite support vector quantile regression estimation

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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

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Acknowledgments

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012S1A3A2033330). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (2012000646) and (2011-0009705).

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Authors and Affiliations

  1. Department of Data Science and Institute of Statistical Information, Inje University, Obang-Dong, Kimhae, 621-749, Korea

    Jooyong Shim & Kyungha Seok

  2. Department of Applied Statistics, Dankook University, Yongin, Gyeonggido, 448-701, Korea

    Changha Hwang

Authors
  1. Jooyong Shim
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  2. Changha Hwang
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  3. Kyungha Seok
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Correspondence to Kyungha Seok.

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Shim, J., Hwang, C. & Seok, K. Composite support vector quantile regression estimation. Comput Stat 29, 1651–1665 (2014). https://doi.org/10.1007/s00180-014-0511-4

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