  In high-dimensional regression modelling, the number of candidate covariates
to be included in the predictor is quite large, and variable selection is
crucial. In this work, we propose a new penalty able to guarantee both sparse
variable selection, i.e. exactly zero regression coefficient estimates, and
quasi-unbiasedness for the coefficients of 'selected' variables in high
dimensional regression models. Simulation results suggest that our proposal
performs no worse than its competitors while always ensuring that the solution
is unique.
