scholarly journals A Distribution-Free Approach to Stochastic Efficiency Measurement with Inclusion of Expert Knowledge

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Kerry Khoo-Fazari ◽  
Zijiang Yang ◽  
Joseph C. Paradi
Keyword(s):  
Data Envelopment Analysis ◽  
Quantitative Data ◽  
Expert Knowledge ◽  
Simulated Dataset ◽  
Data Envelopment ◽  
Nonparametric Modeling ◽  
Distribution Free ◽  
New Frontier ◽  
Distribution Free Approach ◽  
Computationally Intensive

This paper proposes a new efficiency benchmarking methodology that is capable of incorporating probability while still preserving the advantages of a distribution-free and nonparametric modeling technique. This new technique developed in this paper will be known as the DEA-Chebyshev model. The foundation of DEA-Chebyshev model is based on the model pioneered by Charnes, Cooper, and Rhodes in 1978 known as Data Envelopment Analysis (DEA). The combination of normal DEA with DEA-Chebyshev frontier (DCF) can successfully provide a good framework for evaluation based on quantitative data and qualitative intellectual management knowledge. The simulated dataset was tested on DEA-Chebyshev model. It has been statistically shown that this model is effective in predicting a new frontier, whereby DEA efficient units can be further differentiated and ranked. It is an improvement over other methods, as it is easily applied, practical, not computationally intensive, and easy to implement.

Dynamic frontier estimation for monitoring team performances

2019 ◽  
Vol 25 (3/4) ◽  
pp. 212-228
Author(s):  
Melike Yılmaz ◽  
Çağlar Aksezer ◽  
Tankut Atan
Keyword(s):  
Data Envelopment Analysis ◽  
Historical Data ◽  
Expert Knowledge ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Content Type ◽  
Frontier Estimation ◽  
Efficiency Measures ◽  
The Impact

Purpose This paper aims to investigate how predictions of football league standings and efficiency measures of teams, obtained through frontier estimation technique, evolve compared to actual results. Design/methodology/approach The study is based on data from the Turkish first division football league. Historical data for five seasons, from 2011 to 2016, are used to compare weekly estimates to de facto results. Data envelopment analysis efficiency measures are used to estimate team performances. After each week, a data envelopment analysis is run using available data until then, and final team standings are estimated via computed efficiencies. Estimations are improved by using a data envelopment analysis model that incorporates expert knowledge about football. Findings Results indicate that deductions can be made about the league’s future progress. Model incorporating expert knowledge tends to estimate the performance better. Although the prediction accuracy starts out low in early stages, it improves as the season advances. Scatter of individual teams’ performances show fluxional behaviour, which attracts studying the impact of uncontrollable factors such as refereeing. Originality/value While all previous studies focus on season performance, this study handles the problem as a combination of weekly performance and how it converges to reality. By tracking weekly performance, managers get a chance to confront their weak performance indicators and achieve higher ranking by improving on these inefficiencies.

Data Envelopment Analysis Theory, Methodology and Applications

1997 ◽  
Vol 48 (3) ◽  
pp. 332-333 ◽  
Author(s):  
A Charnes ◽  
W Cooper ◽  
A Y Lewin ◽  
L M Seiford
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Analysis Theory
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