Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

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.