EconPapers    
Economics at your fingertips  
 

Sensitivity Analysis in DEA

William W. Cooper (), Shanling Li (), Lawrence Seiford and Joe Zhu ()
Additional contact information
William W. Cooper: University of Texas at Austin
Shanling Li: McGill University
Joe Zhu: Worcester Polytechnic Institute

Chapter Chapter 3 in Handbook on Data Envelopment Analysis, 2011, pp 71-91 from Springer

Abstract: Abstract This chapter presents some of the recently developed analytical methods for studying the sensitivity of DEA results to variations in the data. The focus is on the stability of classification of DMUs (decision making units) into efficient and inefficient performers. Early work on this topic concentrated on developing algorithms for conducting such analyses after it was noted that standard approaches for conducting sensitivity analyses in linear programming could not be used in DEA. However, recent work has bypassed the need for such algorithms. It has also evolved from the early work that was confined to studying data variations in one input or output for one DMU. The newer methods described in this chapter make it possible to analyze the sensitivity of results when all data are varied simultaneously for all DMUs.

Keywords: Data envelopment analysis; Efficiency; Stability; Sensitivity (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations: Track citations by RSS feed

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4419-6151-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9781441961518

DOI: 10.1007/978-1-4419-6151-8_3

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2021-10-16
Handle: RePEc:spr:isochp:978-1-4419-6151-8_3