Cook / Zhu | Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis | Buch | sack.de

Cook / Zhu Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis



Softcover Nachdruck of hardcover 1. Auflage 2007, 334 Seiten, Kartoniert, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 522 g
ISBN: 978-1-4419-4400-9
Verlag: Springer US


Cook / Zhu 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 analytical tool for measuring and evaluating performance. DEA is computational at its core and this book is one of several Springer aim to publish on the subject. This work deals with the micro aspects of handling and modeling data issues in DEA problems. It is a handbook treatment dealing with specific data problems, including imprecise data and undesirable outputs.

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Research


Autoren/Hrsg.


Weitere Infos & Material


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.


Cook, Wade D.
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.


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