Robust Criterion for Multicollinear Factor Selection

• Activity Number: 245 - SLDS CSpeed 4
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Kimon Ntotsis* and Alex Karagrigoriou and Andreas Artemiou
• Companies: University of the Aegean and University of the Aegean and Cardiff University
• Keywords: Multicollinearity Detection; Elastic Net Regularization; Model Selection Criteria; Coefficient Penalization; Factor Over-elimination

Abstract

When it comes to factor interpretation, multicollinearity is among the biggest issues that must be surmounted especially in this new era, of Big Data Analytics. Since even moderate size multicollinearity can prevent a proper interpretation, special diagnostics must be recommended and implemented for identification purposes. In this work, we propose the Elastic Information Criterion which is capable of capturing multicollinearity accurately and effectively without factor over-elimination. The performance in simulated and real numerical studies is demonstrated.