Scalable holistic linear regression
Name
1902.03272.pdf
Description
Accepted version
Size
144.89 KB
Format
Adobe PDF
Checksum (MD5)
8f01ad047bfe22af65214239dcf7864a
Author(s)
Bertsimas, Dimitris J
Li, Michael Lingzhi
Date Issued
May 2020
Journal
Operations Research Letters
Publisher
Elsevier BV
Citation
Bertsimasa, Dimitris and Michael Lingzhi Li. “Scalable holistic linear regression.” Operations Research Letters, 48, 3 (May 2020): 203-208 © 2020 The Author(s)
Version
Author's final manuscript
Abstract
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting algorithm scales with the number of samples n in the 10,000s, compared to the low 100s in the previous framework. Computational results on real and synthetic datasets show it greatly improves from previous algorithms in accuracy, false detection rate, computational time and scalability.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.ORL.2020.02.008
