Model selection in high-dimensional quantile regression with seamless L0L0 penalty

We introduce and study the seamless L0L0 quantile estimator in a linear model when the number of parameters increases with sample size. For this estimator we derive the convergence rate and oracle properties. A consistent BIC criterion to select the tuning parameters is given.