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Using Machine Learning to Select Variables in Data Envelopment Analysis: Simulations and Application Using Electricity Distribution Data
16 references·1 citing·46 second hopsampled
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References

  • 1978Measuring the efficiency of decision making units29,914 cited
  • 2002One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels1,045 cited
  • 2018The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals475 cited
  • 2019Machine learning in energy economics and finance: A review463 cited
  • 2003A multivariate statistical approach to reducing the number of variables in data envelopment analysis293 cited
  • 2009Data Envelopment Analysis as Nonparametric Least-Squares Regression257 cited
  • 1997Model misspecification in Data Envelopment Analysis220 cited
  • 2012Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model163 cited
  • 2018Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using Data Envelopment Analysis: Second stage OLS versus bootstrap approaches158 cited
  • 2011Guidelines for using variable selection techniques in data envelopment analysis157 cited
  • 2009Efficiency analysis of energy networks: An international survey of regulators119 cited
  • 2011When, Where and How to Perform Efficiency Estimation77 cited
  • 2018LASSO variable selection in data envelopment analysis with small datasets74 cited
  • 2006

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Stochastic Nonparametric Envelopment of Data: Combining Virtues of Sfa and DEA in a Unified Framework
74 cited
  • 2017Regulatory Benchmarking: Models, Analyses and Applications59 cited
  • 2021Machine learning and oil price point and density forecasting53 cited
  • Cited by

    • 2024A systemic efficiency measurement of resource management and sustainable practices: A network bias-corrected DEA assessment of OECD countries25 cited