Monotone support vector quantile regression

Jooyong Shim, Kyungha Seok and Changha Hwang

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 10, 5180-5193

Abstract: Quantile regression (QR) models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper we propose support vector quantile regression (SVQR) with monotonicity restriction, which is easily obtained via the dual formulation of the optimization problem. We also provide the generalized approximate cross validation method for choosing the hyperparameters which affect the performance of the proposed SVQR. The experimental results for the synthetic and real data sets confirm the successful performance of the proposed model.

Date: 2017
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DOI: 10.1080/03610926.2015.1096395

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