Efficiency evaluation under uncertainty: a stochastic DEA approach

2020 ◽  
Vol 43 (2) ◽  
pp. 519-538
Author(s):  
P. Beraldi ◽  
M. E. Bruni
Keyword(s):  
Efficiency Evaluation ◽  
Stochastic Dea ◽  
Dea Approach

scholarly journals Two-stage DEA for Bank Efficiency Evaluation Considering Shared Input and Unexpected Output Factors

2020 ◽  
Vol 214 ◽  
pp. 01036
Author(s):  
Song Aifeng ◽  
Zhang XiaoYang ◽  
Huang Weilai ◽  
Yang xue ◽  
Yang Juan
Keyword(s):  
Market Competition ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Uncertain Information ◽  
Two Stage ◽  
Bank Efficiency ◽  
Dea Model ◽  
Stochastic Dea ◽  
Output Factors ◽  
Fierce Competition

With the increasingly fierce market competition, only by relying on high-quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA), as a non-parametric statistical method to effectively deal with multi-input and multi-output problems, has received more and more attention in evaluating the relative efficiency of decision-making units. In the process of bank efficiency evaluation based on DEA method, there will be a situation that banks have both dual role factors and unexpected output factors. The Two-stage DEA model provides an effective analysis method to solve the problem of bank efficiency evaluation of complex organizational structure. In order to evaluate the efficiency of unexpected output with uncertain information, a stochastic DEA model of unexpected output is established.

DEA by sequential exclusion of alternatives in heterogeneous samples

2016 ◽  
Vol 15 (01) ◽  
pp. 5-22 ◽  
Author(s):  
Fuad Aleskerov ◽  
Vsevolod Petrushchenko
Keyword(s):  
Data Envelopment Analysis ◽  
New Method ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Dea Model ◽  
Economic Applications ◽  
Heterogeneous Samples ◽  
Efficiency Estimation ◽  
Dea Approach ◽  
Russian Universities

Data Envelopment Analysis (DEA) is a well-known nonparametric technique of efficiency evaluation which is actively used in many economic applications. However, DEA is not very well applicable when a sample consists of firms operating under drastically different conditions. We offer a new method of efficiency estimation in heterogeneous samples based on a sequential exclusion of alternatives and standard DEA approach. We show a connection between efficiency scores obtained via standard DEA model and the ones obtained via our algorithm. We also illustrate our model by evaluating 28 Russian universities and compare the results obtained by two techniques.

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