  For high-dimensional inference problems, statisticians have a number of
competing interests. On the one hand, procedures should provide accurate
estimation, reliable structure learning, and valid uncertainty quantification.
On the other hand, procedures should be computationally efficient and able to
scale to very high dimensions. In this note, I show that a very simple
data-dependent measure can achieve all of these desirable properties
simultaneously, along with some robustness to the error distribution, in sparse
sequence models.
