Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Restricted support vector quantile regression without crossing

  • Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu) ;
  • Lee, Jang-Taek (Department of Statistics, Dankook University)
  • Received : 2010.09.18
  • Accepted : 2010.11.17
  • Published : 2010.11.30
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Abstract

Quantile regression provides a more complete statistical analysis of the stochastic relationships among random variables. Sometimes quantile functions estimated at different orders can cross each other. We propose a new non-crossing quantile regression method applying support vector median regression to restricted regression quantile, restricted support vector quantile regression. The proposed method provides a satisfying solution to estimating non-crossing quantile functions when multiple quantiles for high dimensional data are needed. We also present the model selection method that employs cross validation techniques for choosing the parameters which aect the performance of the proposed method. One real example and a simulated example are provided to show the usefulness of the proposed method.

Keywords

References

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