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November 2020 A Discussion on Practical Considerations with Sparse Regression Methodologies
Owais Sarwar, Benjamin Sauk, Nikolaos V. Sahinidis
Statist. Sci. 35(4): 593-601 (November 2020). DOI: 10.1214/20-STS806

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.

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Owais Sarwar. Benjamin Sauk. Nikolaos V. Sahinidis. "A Discussion on Practical Considerations with Sparse Regression Methodologies." Statist. Sci. 35 (4) 593 - 601, November 2020. https://doi.org/10.1214/20-STS806

Information

Published: November 2020
First available in Project Euclid: 17 November 2020

MathSciNet: MR4175383
Digital Object Identifier: 10.1214/20-STS806

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.35 • No. 4 • November 2020
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