OR Forum—An Algorithmic Approach to Linear Regression

Dimitris Bertsimas () and Angela King ()
Additional contact information
Dimitris Bertsimas: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Angela King: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Operations Research, 2016, vol. 64, issue 1, 2-16

Abstract: Linear regression models are traditionally built through trial and error to balance many competing goals such as predictive power, interpretability, significance, robustness to error in data, and sparsity, among others. This problem lends itself naturally to a mixed integer quadratic optimization (MIQO) approach but has not been modeled this way because of the belief in the statistics community that MIQO is intractable for large scale problems. However, in the last 25 years (1991–2015), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 450 billion factor speedup in solving mixed integer optimization problems. We present an MIQO-based approach for designing high quality linear regression models that explicitly addresses various competing objectives and demonstrate the effectiveness of our approach on both real and synthetic data sets.

Keywords: integer programming; statistics (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2015.1436 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:64:y:2016:i:1:p:2-16

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().