A Discussion on Practical Considerations with Sparse Regression Methodologies

TL;DR

This paper compares recent studies on sparse regression methods like lasso and subset selection, highlighting their relative strengths and providing guidance for practitioners based on empirical analyses.

Contribution

It synthesizes and compares two recent empirical studies on sparse regression, clarifying their findings and fostering ongoing discussion in the field.

Findings

Different sparse regression methods have varying performance profiles.

Empirical analyses help identify the most suitable methods for specific scenarios.

The paper encourages continued dialogue to improve practical applications.

Abstract

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.