Support vector quantile regression using asymmetric e-insensitive loss function

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 weak point of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide the sparsity Experimental results are then presented; these results illustrate the performance of the proposed method by comparing it with nonsparse SVQR.