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Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction

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  • Adler, Nicole
  • Yazhemsky, Ekaterina
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    Abstract

    Within the data envelopment analysis context, problems of discrimination between efficient and inefficient decision-making units often arise, particularly if there are a relatively large number of variables with respect to observations. This paper applies Monte Carlo simulation to generalize and compare two discrimination improving methods; principal component analysis applied to data envelopment analysis (PCA-DEA) and variable reduction based on partial covariance (VR). Performance criteria are based on the percentage of observations incorrectly classified; efficient decision-making units mistakenly defined as inefficient and inefficient units defined as efficient. A trade-off was observed with both methods improving discrimination by reducing the probability of the latter error at the expense of a small increase in the probability of the former error. A comparison of the methodologies demonstrates that PCA-DEA provides a more powerful tool than VR with consistently more accurate results. PCA-DEA is applied to all basic DEA models and guidelines for its application are presented in order to minimize misclassification and prove particularly useful when analyzing relatively small datasets, removing the need for additional preference information.

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    File URL: http://www.sciencedirect.com/science/article/B6VCT-4W15KS0-1/2/6cf8741e4032b86af9e5404c10a48e78
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 202 (2010)
    Issue (Month): 1 (April)
    Pages: 273-284

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    Handle: RePEc:eee:ejores:v:202:y:2010:i:1:p:273-284

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Data envelopment analysis Principal component analysis Discrimination Simulation;

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    Cited by:
    1. Andor, Mark & Hesse, Frederik, 2011. "A Monte Carlo simulation comparing DEA, SFA and two simple approaches to combine efficiency estimates," CAWM Discussion Papers 51, Center of Applied Economic Research Münster (CAWM), University of Münster.
    2. Orea, Luis & Growitsch, Christian & Jamasb, Tooraj, 2012. "Using Supervised Environmental Composites in Production and Efficiency Analyses: An Application to Norwegian Electricity Networks," EWI Working Papers 2012-18, Energiewirtschaftliches Institut an der Universitaet zu Koeln.
    3. Kao, Ling-Jing & Lu, Chi-Jie & Chiu, Chih-Chou, 2011. "Efficiency measurement using independent component analysis and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 210(2), pages 310-317, April.
    4. Põldaru, Reet & Roots, Jüri, 2014. "A PCA–DEA approach to measure the quality of life in Estonian counties," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 65-73.
    5. Lin, Tzu-Yu & Chiu, Sheng-Hsiung, 2013. "Using independent component analysis and network DEA to improve bank performance evaluation," Economic Modelling, Elsevier, vol. 32(C), pages 608-616.
    6. Mark Andor & Frederik Hesse, . "A Monte Carlo Simulation comparing DEA, SFA and two simple approaches to combine efficiency estimates," Working Papers 201177, Institute of Spatial and Housing Economics, Munster Universitary.
    7. Mark Andor & Frederik Hesse, . "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    8. Andor, Mark & Hesse, Frederik, 2012. "The StoNED age: The departure into a new era of efficiency analysis? An MC study comparing StoNED and the "oldies" (SFA and DEA)," CAWM Discussion Papers 60, Center of Applied Economic Research Münster (CAWM), University of Münster.
    9. Francois-Charles Wolff, 2014. "Lift ticket prices and quality in French ski resorts: Insights from a non-parametric analysis," Working Papers hal-00952999, HAL.

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