Mitigation effect on the efficiency mismeasurement of deterministic data envelopment analysis through statistical noise correction

Seog-Chan Oh; Jaemin Shin; Seog-Chan Oh; Jaemin Shin

doi:10.3934/jimo.2026109

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 6: 2963-2987. doi: 10.3934/jimo.2026109

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Research article

Mitigation effect on the efficiency mismeasurement of deterministic data envelopment analysis through statistical noise correction

Seog-Chan Oh ¹,
Jaemin Shin ^{2
,
,}

1.
Manufacturing Systems Research Lab., General Motors R&D, Warren, MI 48090, USA
2.
Department of Mathematical Sciences, Hanbat National University, Daejeon 34158, South Korea

Received: 04 November 2025 Revised: 14 May 2026 Accepted: 20 May 2026 Published: 27 May 2026
90C08, 90B50

This study proposes a potential method to improve the efficiency measurement accuracy of deterministic data envelopment analysis (DEA) by correcting for the effects of statistical noise on individual decision-making units. Because the solution to a DEA model is obtained at the boundary of the feasible domain, it is susceptible to small statistical noise. Several methods exist for estimating statistical noise; however, this work utilizes the statistical error estimates of stochastic frontier analysis (SFA). The proposed mismeasurement mitigation method is motivated by a stylized equivalence in that, under constant returns to scale with a single output and a single input with no statistical noise, the dual DEA formulation can be conceptualized as a projection of SFA. The results of Monte Carlo simulations based on the Cobb–Douglas model and actual datasets indicate that the proposed method outperforms previous approaches in terms of accuracy under increasing statistical noise.
- Data envelopment analysis (DEA),
- stochastic frontier analysis (SFA),
- mismeasurement mitigation,
- statistical noise correction,
- efficiency mismeasurement
Citation: Seog-Chan Oh, Jaemin Shin. Mitigation effect on the efficiency mismeasurement of deterministic data envelopment analysis through statistical noise correction[J]. Journal of Industrial and Management Optimization, 2026, 22(6): 2963-2987. doi: 10.3934/jimo.2026109

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Abstract

This study proposes a potential method to improve the efficiency measurement accuracy of deterministic data envelopment analysis (DEA) by correcting for the effects of statistical noise on individual decision-making units. Because the solution to a DEA model is obtained at the boundary of the feasible domain, it is susceptible to small statistical noise. Several methods exist for estimating statistical noise; however, this work utilizes the statistical error estimates of stochastic frontier analysis (SFA). The proposed mismeasurement mitigation method is motivated by a stylized equivalence in that, under constant returns to scale with a single output and a single input with no statistical noise, the dual DEA formulation can be conceptualized as a projection of SFA. The results of Monte Carlo simulations based on the Cobb–Douglas model and actual datasets indicate that the proposed method outperforms previous approaches in terms of accuracy under increasing statistical noise.

References

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