Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

In a relatively short period of time, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a whole variety of problems in many different contexts worldwide. This book 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 of both qualitative and quantitative data. This handbook treatment deals with specific data problems including: imprecise or inaccurate data; missing data; qualitative data; outliers; undesirable outputs; quality data; statistical analysis; 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.

There certainly is a need for such a book. We have had issues for a long time on how to deal with computation/data variety problems in the DEA literature The authors are well recognized in the areas and have done substantial research and consulting in the computational and data areas in DEA. I believe the book will be of high quality based upon their authority and experience Wade Cook and Joe Zhu are well known researchers in DEA. Their work spans the history of DEA as well as its range, from theory to practice. They are proposing a timely edited volume inviting "DEA experts on DEA computational and data issues to address important DEA implementation difficulties." The idea for the book is good and is in tune with the evolutionary stage of the topic

Contenu
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 MeasuresClassifying Inputs and Outputs.- Integer Dea Models.- Data Envelopment Analysis With Missing Data.- Preparing Your Data for DEA.