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Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes

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  2. Kumbhakar, Subal C. & Peresetsky, Anatoly & Shchetynin, Yevgenii & Zaytsev, Alexey, 2020. "Technical efficiency and inefficiency: Reassurance of standard SFA models and a misspecification problem," MPRA Paper 102797, University Library of Munich, Germany.
  3. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
  4. Massarutto, Antonio & Grassetti, Luca & Lambardi di San Miniato, Michele & Moletta, Mattia, 2023. "Efficient firms are all alike, but every inefficient firm is such in its own way: Heterogeneity of costs determinants in the Italian water sector," Utilities Policy, Elsevier, vol. 84(C).
  5. Mark A. Andor & David H. Bernstein & Stephan Sommer, 2021. "Determining the efficiency of residential electricity consumption," Empirical Economics, Springer, vol. 60(6), pages 2897-2923, June.
  6. Zhi Li & Lu Lv & Zuo Zhang, 2022. "Research on the Characteristics and Influencing Factors of Chinese Urban Households’ Electricity Consumption Efficiency," Energies, MDPI, vol. 15(20), pages 1-15, October.
  7. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
  8. Otsuka, Akihiro, 2023. "Industrial electricity consumption efficiency and energy policy in Japan," Utilities Policy, Elsevier, vol. 81(C).
  9. Wang, Derek D. & Ren, Yaoyao, 2024. "Accuracy of Deterministic Nonparametric Frontier Models with Undesirable Outputs," European Journal of Operational Research, Elsevier, vol. 315(2), pages 596-612.
  10. Heinz Ahn & Marcel Clermont & Julia Langner, 2025. "Corrected normalized additive analysis as alternative method for easy measurement of efficiency," Journal of Business Economics, Springer, vol. 95(6), pages 777-808, August.
  11. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
  12. Akihiro Otsuka, 2023. "Stochastic demand frontier analysis of residential electricity demands in Japan," Asia-Pacific Journal of Regional Science, Springer, vol. 7(1), pages 179-195, March.
  13. Xian’En Wang & Shimeng Wang & Xipan Wang & Wenbo Li & Junnian Song & Haiyan Duan & Shuo Wang, 2019. "The Assessment of Carbon Performance under the Region-Sector Perspective based on the Nonparametric Estimation: A Case Study of the Northern Province in China," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
  14. Månsson, Kristofer & Qasim, Muhammad & Söderberg, Magnus, 2025. "Are CEOs judged on how cost efficient their firms are?," Energy Economics, Elsevier, vol. 143(C).
  15. Marcos Gonçalves Perroni & Claudimar Pereira da Veiga & Zhaohui Su & Fernando Maciel Ramos & Wesley Vieira da Silva, 2023. "Dynamic Equilibrium of Sustainable Ecosystem Variables: An Experiment," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
  16. Rita, Rui & Marques, Vitor & Bárbara, Diogo & Chaves, Inês & Macedo, Pedro & Moutinho, Victor & Pereira, Mariana, 2023. "Crossing non-parametric and parametric techniques for measuring the efficiency: Evidence from 65 European electricity Distribution System Operators," Energy, Elsevier, vol. 283(C).
  17. Kumbhakar, Subal C. & Peresetsky, A. & Shchetynin, Y. & Zaytsev, A., 2025. "Technical efficiency and inefficiency: Reliability of standard SFA models and a misspecification problem," Econometrics and Statistics, Elsevier, vol. 36(C), pages 55-72.
  18. Tsionas, Mike G., 2021. "Optimal combinations of stochastic frontier and data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 294(2), pages 790-800.
  19. Marcos Gonçalves Perroni & Claudimar Pereira da Veiga & Elaine Forteski & Diego Antonio Bittencourt Marconatto & Wesley Vieira da Silva & Carlos Otávio Senff & Zhaohui Su, 2024. "Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction," SAGE Open, , vol. 14(2), pages 21582440241, June.
  20. Wang, Derek D. & Hu, Peng & Ren, Yaoyao, 2025. "The by-production models for benchmarking," Energy Economics, Elsevier, vol. 143(C).
  21. Yi, Tianhao & Li, Lisha & Li, Zhiyong & Zhang, Jiaxuan, 2025. "Evaluating electricity transmission and distribution efficiency using Data Envelopment Analysis Forest with feature importance," Energy, Elsevier, vol. 330(C).
  22. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
  23. Dong, Hanjiang & Wang, Xiuyuan & Cui, Ziyu & Zhu, Jizhong & Li, Shenglin & Yu, Changyuan, 2025. "Machine learning-enhanced Data Envelopment Analysis via multi-objective variable selection for benchmarking combined electricity distribution performance," Energy Economics, Elsevier, vol. 143(C).
  24. Kosycarz, Ewa & Dędys, Monika & Ekes, Maria & Wranik, Wiesława Dominika, 2023. "The effects of provider contract types and fiscal decentralization on the efficiency of the Polish hospital sector: A data envelopment analysis across 16 health regions," Health Policy, Elsevier, vol. 129(C).
  25. Rüde, Lenard & Wussow, Moritz & Heleno, Miguel & Gust, Gunther & Neumann, Dirk, 2024. "Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data," Energy Policy, Elsevier, vol. 195(C).
  26. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).
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