Journal ArticleDOI
Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe
Nicole Adler,Boaz Golany +1 more
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TLDR
To overcome the difficulties that DEA encounters when there is an excessive number of inputs or outputs, principal component analysis (PCA) is employed to aggregate certain, clustered data, whilst ensuring very similar results to those achieved under the original DEA model.About:
This article is published in European Journal of Operational Research.The article was published on 2001-07-16. It has received 365 citations till now. The article focuses on the topics: Data envelopment analysis & European union.read more
Citations
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Review of ranking methods in the data envelopment analysis context
TL;DR: No one methodology can be prescribed here as the complete solution to the question of ranking, as each technique is useful in a specialist area.
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The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis
TL;DR: In this paper, the authors compared the performance of Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) on the same set of container port data for the world's largest container ports and compared the results obtained.
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An epsilon-based measure of efficiency in DEA – A third pole of technical efficiency
Kaoru Tone,Miki Tsutsui +1 more
TL;DR: In this article, two measures of technical efficiency with different characteristics (radial and non-radial) were proposed. And they were compiled into a composite model called "epsilon-based measure" (EBM).
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Measuring airport quality from the airlines’ viewpoint: an application of data envelopment analysis
Nicole Adler,Joseph Berechman +1 more
TL;DR: In this paper, the authors developed a model to determine the relative efficiency and quality of airports based on data envelopment analysis (DEA), which has been adapted through the use of principle component analysis.
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A multivariate statistical approach to reducing the number of variables in data envelopment analysis
Larry Jenkins,Murray Anderson +1 more
TL;DR: This paper describes a systematic statistical method for deciding which of the original correlated variables can be omitted with least loss of information, and which should be retained.
References
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Journal ArticleDOI
Measuring the efficiency of decision making units
TL;DR: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs and methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs.
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Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis
TL;DR: The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs as mentioned in this paper.
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A procedure for ranking efficient units in data envelopment analysis
TL;DR: In this paper, a modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is developed, which provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods.
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Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions
TL;DR: The construction and analysis of Pareto-efficient frontier production functions by a new Data Envelopment Analysis method is presented in the context of new theoretical characterizations of the inherent structure and capabilities of such empirical production functions.
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An application procedure for DEA
Boaz Golany,Yaakov Roll +1 more
TL;DR: A systematic application procedure of the DEA methodology in its various stages is suggested, focused on the selection of 'decision making units' (DMUs) to enter the analysis as well as the choice and screening of factors.