A novel method of variable selection in data envelopment analysis with entropy measures. (6th August 2021)

• Record Type: Journal Article
• Authors: Deng, Qiang; Lian, Zhaotong; Fu, Qi
• Is Part Of: International journal of operational research. Volume 41:Number 4(2021)
• Page Start: 514
• Page End: 534
• Publication Date: 2021-08-06
• Subjects: data envelopment analysis -- variable selection -- entropy theory -- Akaike information criteria -- AIC; Operations research -- Periodicals; 003.05
• Languages: English
• ISSNs: 1745-7645

Abstract

In data envelopment analysis (DEA) modelling applications, analysts typically experience difficulty in choosing variables when the number of variables is greater than the number of decision-making units (DMUs). In this paper, we develop a novel method to facilitate variable selection in DEA using entropy theory to avoid information redundancy. A numerical analysis is provided to compare our method to those of related studies. The results show that our proposed method produces a lower Akaike information criteria (AIC) value than other approaches. By presenting a real-world case, we show that this new method yields useful managerial results.