A new asymmetric ε-insensitive pinball loss function based support vector quantile regression model

doi:10.1016/j.asoc.2020.106473

A new asymmetric ε-insensitive pinball loss function based support vector quantile regression model

2020-09-01

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DOI：10.1016/j.asoc.2020.106473

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In this paper, we propose a novel asymmetric epsilon-insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the epsilon-insensitive zone approach in it but, they fail to extend the epsilon-insensitive approach for quantile estimation in true sense. The proposed asymmetric epsilon-insensitive pinball loss function can make an asymmetric epsilon- insensitive zone of fixed width around the data and divide it using tau value for the estimation of the tau th quantile. The use of the proposed asymmetric epsilon-insensitive pinball loss function in Support Vector Quantile Regression (SVQR) model improves its prediction ability significantly. It also brings the sparsity back in SVQR model. Further, the numerical results obtained by several experiments carried on simulated and real world datasets empirically show the efficacy of the proposed 'epsilon-Support Vector Quantile Regression' (epsilon-SVQR) model over other existing SVQR models. (C) 2020 Elsevier B.V. All rights reserved.

Keyword:

Quantile regression

Pinball loss function

Support vector machine

epsilon-insensitive loss function

A new asymmetric ε-insensitive pinball loss function based support vector quantile regression model

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