A ν- support vector quantile regression model with automatic accuracy control
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This paper proposes a novel 'ν-support vector quantile regression' (ν-SVQR) model for the quantile estimation. It can facilitate the automatic control over accuracy by creating a suitable asymmetric ϵ-insensitive zone according to the variance present in data. The proposed ν-SVQR model uses the ν fraction of training data points for the estimation of the quantiles. In the ν-SVQR model, training points asymptotically appear above and below of the asymmetric ϵ-insensitive tube in the ratio of 1-τ and τ. Further, there are other interesting properties of the proposed ν-SVQR model, which we have briefly described in this paper. These properties have been empirically verified using the artificial and real world dataset also.
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