An improved Afriat–Diewert–Parkan nonparametric production function estimator
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DOI: 10.1016/j.ejor.2017.07.057
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Cited by:
- Moragues, Raul & Aparicio, Juan & Esteve, Miriam, 2023. "An unsupervised learning-based generalization of Data Envelopment Analysis," Operations Research Perspectives, Elsevier, vol. 11(C).
- Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2019.
"Model averaging estimators for the stochastic frontier model,"
Journal of Productivity Analysis, Springer, vol. 51(2), pages 91-103, June.
- Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2016. "Model Averaging Estimators for the Stochastic Frontier Model," Working Papers 2016-09, University of Miami, Department of Economics.
- José Luis Preciado Arreola & Daisuke Yagi & Andrew L. Johnson, 2020. "Insights from machine learning for evaluating production function estimators on manufacturing survey data," Journal of Productivity Analysis, Springer, vol. 53(2), pages 181-225, April.
- Olesen, O.B. & Ruggiero, J., 2022. "The hinging hyperplanes: An alternative nonparametric representation of a production function," European Journal of Operational Research, Elsevier, vol. 296(1), pages 254-266.
- Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines: a finite-sample study," Operational Research, Springer, vol. 23(3), pages 1-33, September.
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Keywords
Data envelopment analysis; Concave nonparametric frontier estimators; Stochastic DEA; Model averaging; Hinge functions;All these keywords.
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