  High-dimensional prediction typically comprises two steps: variable selection
and subsequent least-squares refitting on the selected variables. However, the
standard variable selection procedures, such as the lasso, hinge on tuning
parameters that need to be calibrated. Cross-validation, the most popular
calibration scheme, is computationally costly and lacks finite sample
guarantees. In this paper, we introduce an alternative scheme, easy to
implement and both computationally and theoretically efficient.
