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• W4313302812 abstract "Data Envelopment Analysis (DEA) presents the typical characteristics of a data-driven approach with the specific objective of determining technical efficiency and production frontiers in Engineering and Microeconomics. However, by construction, the frontier estimator generated by DEA suffers from overfitting problems; something that contrasts with currently accepted models in machine learning. In this regard, DEA can be seen as a preliminary stage of a more complex approach, where the aim is to avoid overfitting in order to determine a proper description of the underlying Data Generating Process that is behind the generation of the observations in a production process. In this paper, we introduce a possible solution to overcome the overfitting problem associated with DEA that is based on cross-validation. This process “peels” the standard DEA frontier (removing certain supporting hyperplanes) until a new convex technology, which also satisfies free disposability in inputs and outputs but not the principle of minimal extrapolation, is determined. Our approach is tested by resorting to a computational experience. Additionally, we illustrate how the new method could be used as a complement to the standard DEA technique through an empirical application based on a PISA (Programme for International Student Assessment) dataset." @default.
• W4313302812 created "2023-01-06" @default.
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• W4313302812 date "2022-12-16" @default.
• W4313302812 modified "2023-09-23" @default.
• W4313302812 title "How to peel a data envelopment analysis frontier: A cross-validation-based approach" @default.
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• W4313302812 doi "https://doi.org/10.1080/01605682.2022.2157765" @default.
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