Data Envelopment Analysis (DEA) Books

V. Charles, J. Aparicio, and J. Zhu, Data Science and Productivity Analytics

V. Charles, J. Aparicio, and J. Zhu, Data Science and Productivity Analytics, Springer, New York, to be published
ISBN:
ISBN-13:


Download Call-for-Chapters (pdf, 300kb)

Call-for-Chapters

Data Science is considered as one of today's most interesting fields of research; a Google search in January 2018 for the string 'data science' with quotation marks returned 24.1 million results, showing an increasing trend over time. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow to use the data to make more intelligent decisions.

On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.

The book aims to bring together a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. These areas are of widespread interest to researchers and practitioners alike.

We wish to invite you to contribute to this edited book. As such, we invite you to submit an extended abstract to cio@umh.es (mentioning in the subject of the email: "Data Science and Productivity Analytics") by 31/July/2018, clearly indicating the objectives of your proposed chapter, its originality, and methodology employed.

IMPORTANT DATES:
31/Jul/2018 Submission deadline for extended abstract
15/Sep/2018 Communication of decisions from editors
01/Dec/2018 Submission of full chapters
28/Feb/2019 Notification of first round of review results
31/May/2019 Submission of revised chapters
15/Jul/2019 Notification of second round of review results
31/Aug/2019 Final decisions notifications

The expected publication date of the book is December 2019.

For further information or clarifications about this Call for Book Chapters, please do not hesitate to contact the Editors directly, via email. (v.charles@buckingham.ac.uk; j.aparicio@umh.es; jzhu@wpi.edu)

J. Zhu and W.D. Cook, Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

J. Zhu 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

About this Book


TABLE OF CONTENTS
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.

G. Gregoriou and Joe Zhu, Evaluating Hedge Funds and CTA Performance: Data Envelopment Analysis Approach

G. Gregoriou and J. Zhu, Evaluating Hedge Funds and CTA Performance: Data Envelopment Analysis Approach John Wiley & Sons, New York, 2005,
ISBN 0-471-68185-7
About this Book

See a book review (pdf, 125kb)

The software included in the book only works under Excel 2003/XP.

D. Sherman and Joe Zhu, Service Productivity Management: Improving Service Performance Using Data Envelopment Analysis

D. Sherman and Joe Zhu, Service Productivity Management: Improving Service Performance Using Data Envelopment Analysis (DEA)
Springer, Boston, 2006,
ISBN  0-387-33211-1


About this Book

TABLE OF CONTENTS
Management of Service Organization Productivity.- Data Envelopment Analysis Explained. - DEA Concepts for Managers.- Solving DEA Using DEAFrontier Software. - DEA Model - Extensions.- Managing Bank Productivity.- Quality-Adjusted DEA (Q-DEA). - Applying DEA to Health Care Organizations.- Government Productivity Management. - Multidimensional Quality-of-Life Measure.- Hedge Fund Performance Evaluation.

The included DEA software works under Excel 97, 2000 and 2003.

The DEA software includes the following DEA models: Envelopment Model, Multiplier Model (with Epsilon), Restricted Multipliers (AR/Cone Ratio Model), Slack-based Model, Measure Specific Model (Uncontrollable factors), and Returns to Scale Estimation.

W.D. Cook and Joe Zhu, Modeling Performance Measurement: Applications and Implementation Issues in DEA

Cook Zhu W.D. Cook and Joe Zhu, Modeling Performance Measurement: Applications and Implementation Issues in DEA
Springer, New York, 2005,
ISBN 0-387-24137-X


About this Book

TABLE OF CONTENTS
Data Envelopment Analysis.- Measuring Efficiency of Highway Maintenance Patrols. - Prioritizing Highway Accident Sites. - Benchmarking Models: Evaluating the effect of e-business activities. - Factor Selection Issues in Bank Branch Performance. - Multicomponent Efficiency Measurement in Banking. - DEA and Multicriteria Decision Modeling.- Modeling Rank Order Data. - Resource Allocation in an R and D Department.- Resource Constrained DEA. - Multicomponent Efficiency: Measurement and core business identification in multiplant firms. - Implementation of Robotics: Identifying Efficient Implementors. - Setting Performance Targets for New DMUs.- Aggregating Preference Rankings. - Ranking Players in Round Robin Tournaments.- Context-Dependent DEA: Models and extension. - Evaluating Power Plant Efficiency: Hierarchical Models.

Book review published in Interfaces (pdf, 83kb)


The included DEA software works under Excel 97, 2000 and 2003.

The DEA software includes the following DEA models: Envelopment Model, Multiplier Model (with Epsilon), Restricted Multipliers (AR/Cone Ratio Model), Slack-based Model, Measure Specific Model (Uncontrollable factors), Returns to Scale Estimation, Context-dependent DEA, Variable-Benchmark DEA Model, and Fixed-Benchmark DEA Model.