Topic
Data envelopment analysis
About: Data envelopment analysis is a research topic. Over the lifetime, 20556 publications have been published within this topic receiving 575617 citations.
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TL;DR: This paper addresses the "super-efficiency" issue of Data Envelopment Analysis by using the slacks-based measure (SBM) of efficiency, which the author proposed in his previous paper [European Journal of Operational Research 130 (2001) 498].
2,575 citations
TL;DR: In this paper, the authors provide a general methodology of bootstrapping in nonparametric frontier models and some adapted methods are illustrated in analyzing the bootstrap sampling variations of input efficiency measures of electricity plants.
Abstract: Efficiency scores of production units are generally measured relative to an estimated pro-duction frontier. Nonparametric estimators (DEA, FDH,···) are based on a finite sample of observed production units. The bootstrap is one easy way to analyze the sensitivity of efficiency scores relative to the sampling variations of the estimated frontier. The main point in order to validate the bootstrap is to define a reasonable data-generating process in this complex framework and to propose a reasonable estimator of it. This paper provides a general methodology of bootstrapping in nonparametric frontier models. Some adapted methods are illustrated in analyzing the bootstrap sampling variations of input efficiency measures of electricity plants.
2,024 citations
31 Jan 2001
TL;DR: In this paper, the slacks-based measure (SBM) of efficiency was proposed to discriminate the efficient decision making units (DMUs) based on the existence of slacks.
Abstract: In most models of Data Envelopment Analysis (DEA), the best performers have the full efficient status denoted by unity (or 100%), and, from experience, we know that usually plural Decision Making Units (DMUs) have this “efficient status”. To discriminate between these efficient DMUs is an interesting subject. This paper addresses this “super-efficiency” issue by using the slacks-based measure (SBM) of efficiency, which the author proposed in his previous paper [European Journal of Operational Research 130 (2001) 498]. The method differs from the traditional one based on the radial measure, e.g. Andersen and Petersen model, in that the former deals directly with slacks in inputs/outputs, while the latter does not take account of the existence of slacks. We will demonstrate the rationality of our approach by comparing it with the radial measure of super-efficiency. The proposed method will be particularly useful when the number of DMUs are small compared with the number of criteria employed for evaluation.
1,979 citations
TL;DR: In this paper, the authors discuss the mathematical programming approach to frontier estimation known as Data Envelopment Analysis (DEA) and examine the effect of model orientation on the efficient frontier.
1,873 citations
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.
1,605 citations