  Sparse linear regression is a vast field and there are many different
algorithms available to build models. Two new papers published in Statistical
Science study the comparative performance of several sparse regression
methodologies, including the lasso and subset selection. Comprehensive
empirical analyses allow the researchers to demonstrate the relative merits of
each estimator and provide guidance to practitioners. In this discussion, we
summarize and compare the two studies and we examine points of agreement and
divergence, aiming to provide clarity and value to users. The authors have
started a highly constructive dialogue, our goal is to continue it.
