Marketing has a tradition in conducting scientific research with cutting-edge techniques developed in management science, such as data envelopment analysis (DEA) (Charnes et al. 1985). Two decades ago, Kamakura, Ratchford, and Agrawal (1988) applied DEA to examine market efficiency and consumer welfare loss. In this review of three new books, my purpose is (1) to provide a background of DEA for...

Data quality is critical to a successful data envelopment analysis (DEA) study. Outlier detection not only identifies suspicious data points and thus prevents the drawing of erroneous conclusions, but also can lead to the discovery of unexpected knowledge. This study develops a unified model to identify outliers in DEA studies by examining how they effect on the boundaries of a data set. The pr...

One of the main problems in efficiency analysis is to determinate the environmental variables that have an impact on the production process. This paper shows that applying bootstrap to data envelopment analysis (DEA) before performing classification and regression trees (CART) increase the quality of the results. In particular, employing data on the Italian Health System, the paper highlights t...

performance evaluation of universities is an important issue between researchers. classic data envelopment analysis (dea) models with deterministic data have been used by many authors to measure efficiency of universities in different countries. however, dea with stochastic data are, rarely used to measure efficiency of universities. in this paper, input oriented model in stochastic data envelo...

The purpose of this study is to evaluate the efficiency indices for 60 Brazilian electricity distribution utilities. These scores are obtained by DEA Data Envelopment Analysis and Bayesian Stochastic Frontier Analysis models, two techniques that can reduce the information asymmetry and improve the regulator’s skill to compare the performance of the utilities, a fundamental aspect in incentive r...

Dear DEA [2] Description: This series of five clinical vignettes focuses on common concerns of clinicians and illustrates the relationship between the Drug Enforcement Administration (DEA) and health professionals, noting that the DEA can help explain federal regulations on legally prescribing controlled substances to patients. Authors: Heit HA, Covington E, Good PM. Source: Pain Medicine field...

The purpose of this paper is to study proposals to use Data Envelopment Analysis (DEA) as a tool for Multiple Criteria Decision Making (MCDM). We ®rst recall, using a simple model, the equivalence between the concept of `ef®ciency' in DEA and that of `convex ef®ciency' in MCDM. Examples are then used to show that various techniques that have been proposed in the DEA literature to deal with MCDM...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...

Data Envelopment Analysis (DEA) is traditionally based on the axiom of convex production possibility sets. In many research situations this axiom is considered overly restrictive, and recent research has focused on finding suitable ways of relaxing it. Unfortunately, there currently is no consensus in the DEA field on why and how to account for non-convexities. This paper explores empirical evi...