Stochastic Data Envelopment Analysis—A review

Olesen, Ole Bent; Petersen, Niels Christian

doi:10.1016/j.ejor.2015.07.058

Cited by 189 publications

(94 citation statements)

References 61 publications

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“…Some observations can be located on the efficient frontier in the deterministic DEA, while some stochastic inputs and outputs are by definition allowed to be around the efficient frontier can be allowed with the aim of conceptualizing the stochastic nature of the data into the model to adapt the measurement and specification errors. Stochastic input and output variations in DEA have been studied within various input-output DEA contexts by many scholars (see e.g., Olesen andPetersen, 2015, Olesen andPetersen, 1995;Huang and Li, 1996;Cooper et al, 1996Cooper et al, , 1998Cooper et al, , 2002Cooper et al, , 2004Land et al, 1993;Morita and Seiford, 1999;Sueyoshi, 2000;Talluri et al, 2006;Olesen, 2006;Bruni et al, 2009;Wu and Lee, 2010;Tsionas and Papadakis, 2010;Udhayakumar et al, 2011). Land et al (1993) were the first to extend the chance-constrained programming (CCP) DEA proposed by Charnes and Cooper (1959), in order to compute efficiency in the presence of uncertainty in which inputs are assumed to be deterministic and outputs are jointly normally distributed.…”

Section: Selective Literaturementioning

confidence: 99%

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

et al. 2018

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Data envelopment analysis (DEA) is a well-known non-parametric technique primarily used to estimate radial efficiency under a set of mild assumptions regarding the production possibility set and the production function. The technical efficiency measure can be complemented with a consistent radial metrics for cost, revenue and profit efficiency in DEA, but only for the setting with known input and output prices. In many real applications of performance measurement, such as the evaluation of utilities, banks and supply chain operations, the input and/or output data are often stochastic and linked to exogenous random variables. It is known from standard results in stochastic programming that rankings of stochastic functions are biased if expected values are used for key parameters. In this paper, we propose economic efficiency measures for stochastic data with known input and output prices. We transform the stochastic economic efficiency models into a deterministic equivalent non-linear form that can be simplified to a deterministic programming with quadratic constraints. An application for a cost minimizing planning problem of a state government in the US is presented to illustrate the applicability of the proposed framework.

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Section: Selective Literaturementioning

confidence: 99%

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

et al. 2018

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“…25 The necessary linear problems to be solved are D 26 This calculation is performed using the FEAR software in R (Wilson 2008), and the bootstrap procedure used in this paper is proposed by Simar and Wilson (1999). A discussion about weaknesses with this bootstrap procedure can be found in Olesen and Petersen (2016). investigating the sources of the TFP change. This can be achieved without problems of sample noise, making the results statistically robust.…”

Section: Bootstrapping the Malmquist Productivity Indexmentioning

confidence: 99%

A bootstrapped Malmquist index applied to Swedish district courts

Mattsson

Månsson

Andersson³

et al. 2018

Eur J Law Econ

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This study measures the total factor productivity (TFP) of the Swedish district courts by applying data envelopment analysis to calculate the Malmquist productivity index (MPI) of 48 Swedish district courts from 2012 to 2015. In contrast to the limited international literature on court productivity, this study uses a fully decomposed MPI. A bootstrapping approach is further applied to compute confidence intervals for each decomposed factor of TFP. The findings show a 1.7% average decline of TFP, annually. However, a substantial variation between years can be observed in the number of statistically significant courts below and above unity. The averages of the components show that the negative impact is mainly driven by negative technical change. Large variations are also observed over time where the small courts have the largest volatility. Two recommendations are: (1) that district courts with negative TFP growth could learn from those with positive

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“…These units in DEA literature are called decision-making units (DMUs) [111,112]. One of the biggest advantages of the DEA method is to enhance the relative efficacy of the decision-making units through optimizing the share of the weighed sum of the outputs to the weighted sum of the inputs [113][114][115][116][117][118][119][120][121]. DEA has a strong discerning ability in finding the valuable information in data [122][123][124][125].…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

et al. 2020

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To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.

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Stochastic Data Envelopment Analysis—A review

Cited by 189 publications

References 61 publications

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

A bootstrapped Malmquist index applied to Swedish district courts

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

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