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

Authors: Qiang Deng; Zhaotong Lian; Qi Fu

Addresses: Faculty of Business Administration, University of Macau, Taipa, SAR, Macau ' Faculty of Business Administration, University of Macau, Taipa, SAR, Macau ' Faculty of Business Administration, University of Macau, Taipa, SAR, Macau

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.

DOI: 10.1504/IJOR.2021.117072

International Journal of Operational Research, 2021 Vol.41 No.4, pp.514 - 534

Received: 29 Jan 2019
Accepted: 19 Mar 2019
Published online: 16 Aug 2021 *

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