Scalable holistic linear regression
Author(s)Bertsimas, Dimitris J; Li, Michael Lingzhi
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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.
DepartmentSloan School of Management; Massachusetts Institute of Technology. Operations Research Center
Operations Research Letters
Bertsimasa, Dimitris and Michael Lingzhi Li. “Scalable holistic linear regression.” Operations Research Letters, 48, 3 (May 2020): 203-208 © 2020 The Author(s)
Author's final manuscript