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Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
. Ed(S): Zhu, Joe; Cook, Wade D. (York University, Toronto, Canada)
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Description for Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
Hardback. In a relatively short period of time, data envelopment analysis (DEA) has grown into a powerful analytical tool for measuring and evaluating performance. DEA is computational at its core. This book deals with the micro aspects of handling and modeling data issues in DEA problems. Editor(s): Zhu, Joe; Cook, Wade D. (York University, Toronto, Canada). Num Pages: 342 pages, 60 black & white illustrations, 79 black & white tables, biography. BIC Classification: KCH; KFFD; KJT; PBUH. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 20. Weight in Grams: 659.
DEA is computational at its core and this book will be one of several books that we will look to publish on the computational aspects of DEA. This book by Zhu and Cook will deal 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. This will be a handbook treatment dealing with specific data problems including the following: (1) imprecise data, (2) inaccurate data, (3) missing ... Read more
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Format
Hardback
Publication date
2007
Publisher
Springer-Verlag New York Inc. United States
Number of pages
342
Condition
New
Number of Pages
334
Place of Publication
New York, NY, United States
ISBN
9780387716060
SKU
V9780387716060
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
From the reviews: "This book collects 17 articles that study data envelopment analysis (DEA) techniques. … Those working with and already familiar with DEA methods may find the book more useful." (Robert Lund, Journal of the American Statistical Association, Vol. 103 (484), December 2008)