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The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis

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  • Charles, Vincent
  • Aparicio, Juan
  • Zhu, Joe

Abstract

Data envelopment analysis (DEA) is a technique for identifying the best practices of a given set of decision-making units (DMUs) whose performance is categorized by multiple performance metrics that are classified as inputs and outputs. Although DEA is regarded as non-parametric, the sample size can be an issue of great importance in determining the efficiency scores for the evaluated units, empirically, when the use of too many inputs and outputs may result in a significant number of DMUs being rated as efficient. In the DEA literature, empirical rules have been established to avoid too many DMUs being rated as efficient. These empirical thresholds relate the number of variables with the number of observations. When the number of DMUs is below the empirical threshold levels, the discriminatory power among the DMUs may weaken, which leads to the data set not being suitable to apply traditional DEA models. In the literature, the lack of discrimination is often referred to as the “curse of dimensionality”. To overcome this drawback, we provide a simple approach to increase the discriminatory power between efficient and inefficient DMUs using the well-known pure DEA model, which considers either inputs only or outputs only. Three real cases, namely printed circuit boards, Greek banks, and quality of life in Fortune’s best cities, have been discussed to illustrate the proposed approach.

Suggested Citation

  • Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
  • Handle: RePEc:eee:ejores:v:279:y:2019:i:3:p:929-940
    DOI: 10.1016/j.ejor.2019.06.025
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    References listed on IDEAS

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    Cited by:

    1. Yongxiu He & Meiyan Wang & Fengtao Guang, 2019. "Applicability Evaluation of China’s Retail Electricity Price Package Combining Data Envelopment Analysis and a Cloud Model," Energies, MDPI, Open Access Journal, vol. 13(1), pages 1-21, December.
    2. Joe Zhu, 0. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 0, pages 1-23.
    3. Nickolaos G. Tzeremes, 2019. "Technological change, technological catch-up and export orientation: evidence from Latin American Countries," Journal of Productivity Analysis, Springer, vol. 52(1), pages 85-100, December.
    4. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, Open Access Journal, vol. 12(3), pages 1-24, January.
    5. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP092020, School of Economics, University of Queensland, Australia.

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