Data Envelopment Analysis
A Handbook of Models and Methods
(Sprache: Englisch)
This handbook represents a milestone in the progression of Data Envelopment Analysis (DEA). Written by experts who are often major contributors to DEA theory, it includes a collection of chapters that represent the current state-of-the-art in DEA research....
Voraussichtlich lieferbar in 3 Tag(en)
versandkostenfrei
Buch (Kartoniert)
164.99 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenlose Rücksendung
- Ratenzahlung möglich
Produktdetails
Produktinformationen zu „Data Envelopment Analysis “
Klappentext zu „Data Envelopment Analysis “
This handbook represents a milestone in the progression of Data Envelopment Analysis (DEA). Written by experts who are often major contributors to DEA theory, it includes a collection of chapters that represent the current state-of-the-art in DEA research. Topics include distance functions and their value duals, cross-efficiency measures in DEA, integer DEA, weight restrictions and production trade-offs, facet analysis in DEA, scale elasticity, benchmarking and context-dependent DEA, fuzzy DEA, non-homogenous units, partial input-output relations, super efficiency, treatment of undesirable measures, translation invariance, stochastic nonparametric envelopment of data, and global frontier index.Focusing only on new models/approaches of DEA, the book includes contributions from Juan Aparicio, Mette Asmild, Yao Chen, Wade D. Cook, Juan Du, Rolf Färe, Julie Harrison, Raha Imanirad, Andrew Johnson, Chiang Kao, Abolfazl Keshvari, Timo Kuosmanen, Sungmook Lim, Wenbin Liu, Dimitri Margaritis, Reza Kazemi Matin, Ole B. Olesen, Jesus T. Pastor, Niels Chr. Petersen, Victor V. Podinovski, Paul Rouse, Antti Saastamoinen, Biresh K. Sahoo, Kaoru Tone, and Zhongbao Zhou.
Inhaltsverzeichnis zu „Data Envelopment Analysis “
Distance Functions in Primal and Dual Spaces.- DEA Cross Efficiency.- DEA Cross Efficiency Under Variable Returns to Scale.- Discrete and Integer Valued Inputs and Outputs in Data Envelopnebt Analysis.- DEA Models with Production Trade-offs and Weight Restrictions.- Facet Analysis in Data Envelopment Analysis.- Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework.- Translation Invariance in Data Envelopment Analysis.- Scale Elasticity in Non-parametric DEA Approach.- DEA Based Benchmarking Models.- Data Envelopment Analysis with Non-Homogeneous DMUs.- Efficiency Measurement in Data Envelopment Analysis with Fuzzy Data.- Partial Input to Output Impacts in DEA: Production Considerations and Resource Sharing Among Business Sub-Units.- Super-efficiency in Data Envelopment Analysis.- DEA Models with Undesirable Inputs.- Frontier Differences and the Global Malmquist Index.
Autoren-Porträt
Professor Joe Zhu is one of the prominent researchers in the field of Data Envelopment Analysis (DEA). His research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published over 100 articles in peer-reviewed journals including Operations Research, Sloan Management Review, European Journal of Operational Research, Journal of the Operational Research Society, Naval Research Logistics, IIE Transactions, Journal of Banking and Finance, OMEGA, and others. He is an Area Editor of OMEGA, an Associate Editor of INFOR, and the Associate Series Editor of Springer's International Series in Operations Research and Management Science. He is a Japan Society for Promotion of Science (JSPS) fellow and a William Evans Visiting Fellow of University of Otago, New Zealand. His research has been supported by KPMG Foundation, National Institute of Health, and Department of Veterans Affairs.
Bibliographische Angaben
- 2016, Softcover reprint of the original 1st ed. 2015, XII, 465 Seiten, 18 farbige Abbildungen, Maße: 15,6 x 23,6 cm, Kartoniert (TB), Englisch
- Herausgegeben: Joe Zhu
- Verlag: Springer, Berlin
- ISBN-10: 1489978453
- ISBN-13: 9781489978455
Sprache:
Englisch
Kommentar zu "Data Envelopment Analysis"
Schreiben Sie einen Kommentar zu "Data Envelopment Analysis".
Kommentar verfassen