Here is the function I want to minimize:
$$\sum_i\frac{\rho_{\tau}(1-\alpha-\pmb x_i^{\top}\pmb\beta)}{\sqrt{1+\pmb\beta^{\top}\pmb\beta}}$$
where $\alpha\in\mathbb{R}$, $\pmb\beta\in\mathbb{R}^p$ are the parameters we want to optimize over and $\tau\in(0,1)$ and the $\pmb x_i$ are given and:
$$\rho_{\tau}(x)=\tau\max(0,x)+(1-\tau)\max(-x,0)$$
It is a combination of square root of L2 terms (the denominator) and L1 terms (the numerator). It is not clear to me if this problem can be framed as a convex optimization one (I guess SOCP) and if yes how to reformulate so that it is in standard (SOCP?) form. I guess an answer to either one of these two questions is what I am looking for.
Edit:
This is the objective function described in: He, Xuming; Liang, Hua (1997). Quantile regression estimates for a class of linear and partially linear errors-in-variables models. Ungated copy.