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Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

  • Joe Zhu
  • Wade D. Cook

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Wade D. Cook, Joe Zhu
    Pages 13-34
  3. Yao Chen, Joe Zhu
    Pages 35-62
  4. Jesús T. Pastor, José L. Ruiz
    Pages 63-84
  5. John Ruggiero
    Pages 85-101
  6. Zhongsheng Hua, Yiwen Bian
    Pages 103-121
  7. Isabelle Piot-Lepetit, Monique Le Moing
    Pages 123-138
  8. Nicole Adler, Boaz Golany
    Pages 139-153
  9. José H. Dulá
    Pages 155-170
  10. Nicole Adler, Adi Raveh, Ekaterina Yazhemsky
    Pages 171-187
  11. Wade D. Cook, Liang Liang, Feng Yang, Joe Zhu
    Pages 189-208
  12. Rolf Färe, Shawna Grosskopf, Gerald Whittaker
    Pages 209-240
  13. Hiroshi Morita, Joe Zhu
    Pages 241-259
  14. Wade D. Cook, Joe Zhu
    Pages 261-270
  15. Sebastián Lozano, Gabriel Villa
    Pages 271-289
  16. Chiang Kao, Shiang-Tai Liu
    Pages 291-304
  17. Joe Sarkis
    Pages 305-320
  18. Back Matter
    Pages 321-330

About this book

Introduction

In a relatively short period of time, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a whole variety of problems in many different contexts worldwide. The analysis of an array of these problems has been resistant to other methodological approaches because of the multiple levels of complexity that must be considered. Several examples of multifaceted problems in which DEA analysis has been successfully used are: (1) maintenance activities of US Air Force bases in geographically dispersed locations, (2) policy force efficiencies in the United Kingdom, (3) branch bank performances in Canada, Cyprus, and other countries and (4) the efficiency of universities in performing their education and research functions in the U.S., England, and France. In addition to localized problems, DEA applications have been extended to performance evaluations of 'larger entities' such as cities, regions, and countries. These extensions have a wider scope than traditional analyses because they include "social" and "quality-of-life" dimensions which require the modeling of qualitative and quantitative data in order to analyze the layers of complexity for an evaluation of performance and to provide solution strategies.

DEA is computational at its core and this book by Zhu and Cook deals with the micro aspects of handling and modeling data issues in modeling DEA problems. DEA's use has grown with its capability of dealing with complex "service industry" and the "public service domain" types of problems that require modeling both qualitative and quantitative data. It is a handbook treatment dealing with specific data problems including the following: (1) imprecise data, (2) inaccurate data, (3) missing data, (4) qualitative data, (5) outliers, (6) undesirable outputs, (7) quality data, (8) statistical analysis, (9) software and other data aspects of modeling complex DEA problems. In addition, the book demonstrates how to visualize DEA results when the data is more than 3-dimensional, and how to identify efficiency units quickly and accurately.

Keywords

Data Envelopment Analysis Data-Envelopment-Analysis STATISTICA benchmarking calculus data data envelopment efficiency modeling statistical analysis

Editors and affiliations

  • Joe Zhu
    • 1
  • Wade D. Cook
    • 2
  1. 1.Worcester Polytechnic InstituteUSA
  2. 2.York UniversityCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-71607-7
  • Copyright Information Springer Science+Business Media, LLC 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Business and Economics
  • Print ISBN 978-0-387-71606-0
  • Online ISBN 978-0-387-71607-7
  • Buy this book on publisher's site