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Support Vector Quantile Regression with Weighted Quadratic Loss Function

  • Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu) ;
  • Hwang, Chang-Ha (Department of Statistics, Dankook University)
  • Received : 20091000
  • Accepted : 20091100
  • Published : 2010.03.31

Abstract

Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.

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Cited by

  1. Weighted quantile regression via support vector machine vol.42, pp.13, 2015, https://doi.org/10.1016/j.eswa.2015.03.003