Data Irregularities And Structural Complexities In Dea

Wade D. Cook () and Joe Zhu ()
Additional contact information
Wade D. Cook: York University
Joe Zhu: Worcester Polytechnic Institute

Chapter Chapter 1 in Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, 2007, pp 1-11 from Springer

Abstract: Abstract Over the recent years, we have seen a notable increase in interest in data envelopment analysis (DEA) techniques and applications. Basic and advanced DEA models and techniques have been well documented in the DEA literature. This edited volume addresses how to deal with DEA implementation difficulties involving data irregularities and DMU structural complexities. Chapters in this volumes address issues including the treatment of ordinal data, interval data, negative data and undesirable data, data mining and dimensionality reduction, network and supply chain structures, modeling non-discretionary variables and flexible measures, context-dependent performance, and graphical representation of DEA.

Keywords: Data Envelopment Analysis (DEA); Ordinal Data; Interval Data; Data Mining; Efficiency; Flexible; Supply Chain; Network; Undesirable (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations: View citations in EconPapers (116)

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:sprchp:978-0-387-71607-7_1

Ordering information: This item can be ordered from
http://www.springer.com/9780387716077

DOI: 10.1007/978-0-387-71607-7_1

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().