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.

翻译：简单线性回归是一个广阔的领域,有许多不同的算法可以建立模型。在《统计科学》中发表的两份新论文研究了一些稀薄回归方法的比较性能,包括拉索和子集选择。综合经验分析使研究人员能够展示每个估算者的相对优点,并向从业人员提供指导。在本次讨论中,我们总结和比较了这两项研究,并研究了共识和分歧点,目的是为用户提供清晰度和价值。作者们已经开始了一场非常建设性的对话,我们的目标是继续这样做。