[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2011.18.2.165

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)

Publication Information

Communications for Statistical Applications and Methods / v.18, no.2, 2011 , pp. 165-170 More about this Journal

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.

Keywords

Asymmetric e-insensitive loss function; quantile regression; support vector machine; support vector quantile regression;

Citations & Related Records

Reference

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