A $ν$- support vector quantile regression model with automatic accuracy control
This work addresses quantile regression for statistical modeling, offering an incremental improvement with automatic accuracy control.
The paper tackles the problem of quantile estimation by proposing a ν-support vector quantile regression model that automatically controls accuracy through an asymmetric ε-insensitive zone based on data variance, using a ν fraction of training points to estimate quantiles with asymptotic ratios of 1-τ and τ above and below the tube.
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.