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

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  • Qiang Deng
  • Zhaotong Lian
  • Qi Fu

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.

Suggested Citation

  • Qiang Deng & Zhaotong Lian & Qi Fu, 2021. "A novel method of variable selection in data envelopment analysis with entropy measures," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 41(4), pages 514-534.
    • Handle: RePEc:ids:ijores:v:41:y:2021:i:4:p:514-534
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    Cited by:

    1. Qiang Deng, 2024. "A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis," Mathematics, MDPI, vol. 12(18), pages 1-15, September.

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