Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
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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)…mehr

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Produktbeschreibung
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.
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Autorenporträt
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 ac

curately.
Rezensionen
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)