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DEA Models Overview

In: Data Envelopment Analysis in the Financial Services Industry

Author

Listed:
  • Joseph C. Paradi

    (University of Toronto)

  • H. David Sherman

    (Northeastern University)

  • Fai Keung Tam

    (University of Toronto)

Abstract

We begin with the basic DEA Models and some useful extensions (although we expect that some will see it as too much while others as too little). While we promised to minimize the mathematics, some are, unfortunately, unavoidable. We have excluded any specific discussion of the underlying linear programming (LP) mathematics that drives DEA, and while some general understanding of this is helpful for understanding the academic literature, it is not needed to understand the benefits and ways to apply DEA.

Suggested Citation

  • Joseph C. Paradi & H. David Sherman & Fai Keung Tam, 2018. "DEA Models Overview," International Series in Operations Research & Management Science, in: Data Envelopment Analysis in the Financial Services Industry, chapter 0, pages 3-39, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-69725-3_1
    DOI: 10.1007/978-3-319-69725-3_1
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    Cited by:

    1. Milica Jovanović & Gordana Savić & Yuzhuo Cai & Maja Levi-Jakšić, 2022. "Towards a Triple Helix based efficiency index of innovation systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2577-2609, May.

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