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Zhu, J. and W.D. Cook, Modeling Data
Irregularities and Structural Complexities in Data Envelopment
Analysis, Springer, Boston, 2007
ISBN: 0387716068
ISBN-13: 978-0-387-71606-0
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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. The analysis of an array of these problems has been
resistant to other methodological approaches because of the multiple
levels of complexity that must be considered. Several examples of
multifaceted problems in which DEA analysis has been successfully used
are: (1) maintenance activities of US Air Force bases in geographically
dispersed locations, (2) policy force efficiencies in the United
Kingdom, (3) branch bank performances in Canada, Cyprus, and other
countries and (4) the efficiency of universities in performing their
education and research functions in the U.S., England, and France. In
addition to localized problems, DEA applications have been extended to
performance evaluations of 'larger entities' such as cities, regions,
and countries. These extensions have a wider scope than traditional
analyses because they include "social" and "quality-of-life" dimensions
which require the modeling of qualitative and quantitative data in
order to analyze the layers of complexity for an evaluation of
performance and to provide solution strategies.
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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