A ν- support vector quantile regression model with automatic accuracy control

Abstract

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

Tasks

Reproductions