A Discussion on Practical Considerations with Sparse Regression Methodologies

• Sarwar, Owais ^[1] ; Sauk, Benjamin ^[1] ; Sahinidis, Nikolaos V. ^[2]
  1. [1] Carnegie Mellon University

     Carnegie Mellon University

     City of Pittsburgh, Estados Unidos

     
  2. [2] Georgia Institute of Technology

     Georgia Institute of Technology

     Estados Unidos

     
• Localización: Statistical science, ISSN 0883-4237, Vol. 35, Nº. 4, 2020, págs. 593-601
• Idioma: inglés
• DOI: 10.1214/20-STS806
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.