A novel method of variable selection in data envelopment analysis with entropy measures

Qiang Deng, Zhaotong Lian and Qi Fu

International Journal of Operational Research, 2021, vol. 41, issue 4, 514-534

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.

Keywords: data envelopment analysis; variable selection; entropy theory; Akaike information criteria; AIC. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=117072 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijores:v:41:y:2021:i:4:p:514-534

Access Statistics for this article

More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().