Cover image for Advances in DEA theory and applications : with extensions to forecasting models
Title:
Advances in DEA theory and applications : with extensions to forecasting models
Author:
Tone, Kaoru, 1931- editor.
ISBN:
9781118946701

9781118946695

9781118946688
Edition:
First edition.
Physical Description:
1 online resource
Series:
Wiley series in operations research and management science
Contents:
Title Page ; Copyright Page ; Contents; List of Contributors; About the Authors; Preface; Part 1 DEA Theory ; Chapter 1 Radial DEA Models; 1.1 Introduction; 1.2 Basic Data; 1.3 Input-Oriented CCR Model; 1.3.1 The CRS Model; 1.4 The Input-Oriented BCC Model; 1.4.1 The VRS Model; 1.5 The Output-Oriented Model; 1.6 Assurance Region Method; 1.7 The Assumptions behind Radial Models; 1.8 A Sample Radial Model; References; Chapter 2 Non-Radial DEA Models; 2.1 Introduction; 2.2 The SBM Model; 2.2.1 Input-Oriented SBM; 2.2.2 Output-Oriented SBM; 2.2.3 Non-Oriented SBM; 2.3 An Example of an SBM Model.

2.4 The Dual Program of the SBM Model2.5 Extensions of the SBM Model; 2.5.1 Variable-Returns-to-Scale (VRS) Model; 2.5.2 Weighted-SBM Model; 2.6 Concluding Remarks; References; Chapter 3 Directional Distance DEA Models; 3.1 Introduction; 3.2 Directional Distance Model; 3.3 Variable-Returns-to-Scale DD Models; 3.4 Slacks-Based DD Model; 3.5 Choice of Directional Vectors; References; Chapter 4 Super-Efficiency DEA Models; 4.1 Introduction; 4.2 Radial Super-Efficiency Models; 4.2.1 Input-Oriented Radial Super-Efficiency Model; 4.2.2 Output-Oriented Radial Super-Efficiency Model.

4.2.3 Infeasibility Issues in the VRS Model4.3 Non-radial Super-Efficiency Models; 4.3.1 Input-Oriented Non-Radial Super-Efficiency Model; 4.3.2 Output-Oriented Non-Radial Super-Efficiency Model; 4.3.3 Non-Oriented Non-Radial Super-Efficiency Model; 4.3.4 Variable-Returns-to-Scale Models; 4.4 An Example of a Super-Efficiency Model; References; Chapter 5 Determining Returns to Scale in the VRS DEA Model; 5.1 Introduction; 5.2 Technology Specification and Scale Elasticity; 5.2.1 Technology; 5.2.2 Measure of Scale Elasticity; 5.2.3 Scale Elasticity in DEA Models; 5.3 Summary; References.

Chapter 6 Malmquist Productivity Index Models6.1 Introduction; 6.2 Radial Malmquist Model; 6.3 Non-Radial and Oriented Malmquist Model; 6.4 Non-Radial and Non-Oriented Malmquist Model; 6.5 Cumulative Malmquist Index (CMI); 6.6 Adjusted Malmquist Index (AMI); 6.7 Numerical Example; 6.7.1 DMU A; 6.7.2 DMU B; 6.7.3 DMU C; 6.7.4 DMU D; 6.8 Concluding Remarks; References; Chapter 7 The Network DEA Model; 7.1 Introduction; 7.2 Notation and Production Possibility Set; 7.3 Description of Network Structure; 7.3.1 Inputs and Outputs; 7.3.2 Links; 7.4 Objective Functions and Efficiencies.

7.4.1 Input-Oriented Case7.4.2 Output-Oriented Case; 7.4.3 Non-Oriented Case; Reference; Chapter 8 The Dynamic DEA Model; 8.1 Introduction; 8.2 Notation and Production Possibility Set; 8.3 Description of Dynamic Structure; 8.3.1 Inputs and Outputs; 8.3.2 Carry-Overs; 8.4 Objective Functions and Efficiencies; 8.4.1 Input-Oriented Case; 8.4.2 Output-Oriented Case; 8.4.3 Non-Oriented Case; 8.5 Dynamic Malmquist Index; 8.5.1 Dynamic Catch-up Index; 8.5.2 Dynamic Frontier Shift Effect; 8.5.3 Dynamic Malmquist Index; 8.5.4 Dynamic Cumulative Malmquist Index; 8.5.5 Dynamic Adjusted Malmquist Index.
Abstract:
Advances in DEA Theory and Applications provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting.
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John Wiley and Sons
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