Data envelopment analysis (DEA) is widely used as a benchmarking tool for improving productive performance of decision making units (DMUs). The benchmarks produced by DEA are obtained as a sideproduct of computing efficiency scores. As a result, the benchmark units may differ from the evaluated DMU in terms of their input–output profiles and the scale size. Moreover, the DEA benchmarks may oper...

Electricity distribution network is a prime example of a natural local monopoly. In many countries, electricity distribution firms are regulated by the government. In Finland, the regulator estimates the efficient cost frontier using the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) methods. This paper reports the main results of the research project commissioned by the...

The field of production frontier estimation is divided between the parametric Stochastic Frontier Analysis (SFA) and the deterministic, nonparametric Data Envelopment Analysis (DEA). This paper explores an amalgam of DEA and SFA that melds a nonparametric frontier with a stochastic composite error. Our model imposes the standard SFA assumptions for the inefficiency and noise terms. The frontier...

By employing the non-stochastic data envelopment analysis (DEA) method, the present paper provides empirical evidence on the efficiency of the Malaysian banking sector around the Asian financial crisis. The empirical findings suggest that the foreign banks (FB) have exhibited higher technical efficiency (TE) compared to their domestic bank counterparts. However, the results suggest that the FB ...

Copyright 2005 by Helena Johansson. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Abstract Technical, allocative, and economic input efficiency scores were estimated for an unbalanced panel of Swedish dairy farms, using data envelopment analysis (DEA) and the stocha...

Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state-contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition t...

Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of homogenous Decision Making Units (DMUs) with multiple inputs and multiple outputs. These factors may be evaluated in fuzzy or stochastic environment. Hence, the classic structures of DEA model may be changed where in two fold fuzzy stochastic environment. For instances, linearity, feasibility a...

DEA (Data Envelopment Analysis) is a technique for evaluating the relative effectiveness of decision-making units (DMU) with multiple inputs and outputs data based on non-parametric modeling using mathematical programming (including linear programming, multi-parameter programming, stochastic programming, etc.). The classical DEA methods are developed to handle the information in the form of cri...

This paper uses both the non-parametric method of Data Envelopment Analysis (DEA) and the econometric method of Stochastic Frontier Analysis (SFA) to study the production technology and cost efficiency of the U.S. dental care industry using practice level data. The American Dental Association (ADA) 2006 survey data for a number of general dental practices in Colorado are used for the empirical ...

Our motivation is to detail a potential improvement on the three-stage analysis published by Fried et al. [Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 2002;17:157–74] that can distinguish true performers from those that may be advantaged by favourable environments or measurement errors. The method starts with data env...