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 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 will demonstrate how to visualize DEA results when the data is more than 3-dimensional, and how to identify efficiency units quickly and accurately.
Rezensionen / Stimmen
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)
Sprache
Verlagsort
Verlagsgruppe
Illustrationen
60
60 s/w Abbildungen
VIII, 334 p. 60 illus.
Dateigröße
ISBN-13
978-0-387-71607-7 (9780387716077)
DOI
10.1007/978-0-387-71607-7
Schweitzer Klassifikation
Data Irregularities And Structural Complexities In Dea.- Rank Order Data In Dea.- Interval And Ordinal Data.- Variables With Negative Values In Dea.- Non-Discretionary Inputs.- DEA with Undesirable Factors.- European Nitrate Pollution Regulation and French Pig Farms' Performance.- PCA-DEA.- Mining Nonparametric Frontiers.- DEA Presented Graphically Using Multi-Dimensional Scaling.- DEA Models For Supply Chain or Multi-Stage Structure.- Network DEA.- Context-Dependent Data Envelopment Analysis and its Use.- Flexible Measures-Classifying Inputs and Outputs.- Integer Dea Models.- Data Envelopment Analysis With Missing Data.- Preparing Your Data for DEA.
