论文标题
关于实际考虑的讨论，回归方法很少
A Discussion on Practical Considerations with Sparse Regression Methodologies
论文作者
论文摘要
稀疏线性回归是一个广阔的领域，并且有许多不同的算法可以构建模型。两篇新论文在统计科学研究中发表了几种稀疏回归方法的比较性能，包括拉索和子集选择。全面的经验分析使研究人员能够证明每个估计器的相对优点，并为从业人员提供指导。在讨论中，我们总结并比较了这两项研究，并研究了一致性和分歧的要点，旨在为用户提供清晰度和价值。作者已经开始了高度建设性的对话，我们的目标是继续进行对话。
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.
