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Dea数据标准化方法及其在方向距离函数模型中的应用
[Data normalization for data envelopment analysis and its application to directional distance function]

Author

Listed:
  • Cheng, Gang
  • Qian, Zhenhua

Abstract

Directional distance function is the generalization of radial model in data envelopment analysis. It has the capacity of dealing with undesirable outputs, but the problem is that it has no unit-invariant measurement of efficiency, which hampers its application to empirical studies. Data normalization for data envelopment analysis is a universal solution for the problem of unit-invariance, and the efficiency keeps unchanged in radial and non-radial models after data normalization. A unit-invariant efficiency measure for directional distance function is developed based on DEA data normalization.

Suggested Citation

  • Cheng, Gang & Qian, Zhenhua, 2011. "Dea数据标准化方法及其在方向距离函数模型中的应用 [Data normalization for data envelopment analysis and its application to directional distance function]," MPRA Paper 31995, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:31995
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    File URL: https://mpra.ub.uni-muenchen.de/42137/1/MPRA_paper_42137.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Data Envelopment Analysis; Data Normalization; Units Invariance; Directional Distance Function;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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