Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives

2007 ◽  
Vol 180 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Janet M. Wagner ◽  
Daniel G. Shimshak
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Stepwise Selection ◽  
Selection Of Variables ◽  
Selection Of

Selection of Variables in Data Envelopment Analysis for Evaluation of Stock Performance

2021 ◽  
pp. 0258042X2110025
Author(s):  
B. Senthil Arasu ◽  
Desti Kannaiah ◽  
Nancy Christina J. ◽  
Malik Shahzad Shabbir
Keyword(s):  
Data Envelopment Analysis ◽  
Stock Exchange ◽  
Secondary Market ◽  
Data Envelopment ◽  
Stock Performance ◽  
The Past ◽  
Financial Strength ◽  
Selection Of Variables ◽  
Selection Of ◽  
Past Price

Data envelopment analysis (DEA) is a relative measurement technique used to evaluate the efficiencies of a homogeneous group of samples with multiple inputs and/or outputs. DEA can be highly effective when right variables are chosen. The objective of this study is to identify the most appropriate variables for DEA to evaluate stock performance and find the efficient ones from a pool of stocks. Evaluation of stocks are carried out either by assessing their financial strength or by assessing their past price behaviour in the secondary market or both. In any case, it is imperative to use suitable variables to evaluate the performance of stocks. For this purpose, three different combinations of variables were tested on 69 non-financial stocks listed in the National Stock Exchange (NSE), which were selected based on their market capitalization. The results obtained suggest that all the three sets of variables taken for the study help in the identification of efficient stocks. The average returns of the stocks selected in all the three cases are higher than the market return. Among the three sets, stocks identified using the past price behaviour give a higher return when compared to the other two sets. The study can help academicians and investors to percolate efficient stocks from a large pool of stocks. The selected stocks can be further analysed to construct an effective portfolio.

scholarly journals A Comparison of Ensemble and Dimensionality Reduction DEA Models Based on Entropy Criterion

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 232
Author(s):  
Parag C. Pendharkar
Keyword(s):  
Dimensionality Reduction ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Objective Functions ◽  
High Entropy ◽  
Selection Of Variables ◽  
Dea Models ◽  
Entropy Criterion ◽  
Selection Of ◽  
Normally Distributed

Dimensionality reduction research in data envelopment analysis (DEA) has focused on subjective approaches to reduce dimensionality. Such approaches are less useful or attractive in practice because a subjective selection of variables introduces bias. A competing unbiased approach would be to use ensemble DEA scores. This paper illustrates that in addition to unbiased evaluations, the ensemble DEA scores result in unique rankings that have high entropy. Under restrictive assumptions, it is also shown that the ensemble DEA scores are normally distributed. Ensemble models do not require any new modifications to existing DEA objective functions or constraints, and when ensemble scores are normally distributed, returns-to-scale hypothesis testing can be carried out using traditional parametric statistical techniques.

scholarly journals Evaluation of the Efficiency of Basic Sanitation Integrated Management in Brazilian Municipalities

2020 ◽  
Vol 17 (24) ◽  
pp. 9244
Author(s):  
Alvaro Cavalcanti ◽  
Arthur Teixeira ◽  
Karen Pontes
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Integrated Management ◽  
Financial Resources ◽  
Data Envelopment ◽  
Multiple Data ◽  
Basic Sanitation ◽  
Potential Efficiency ◽  
Support Decision Making ◽  
Selection Of

This study aims to evaluate the level of technical efficiency of companies that perform the integrated management of basic sanitation in Brazilian municipalities. A Multiple Data Envelopment Analysis (M-DEA) model was applied to estimate the performance of water supply and sewage services in 1628 municipalities covering more than 56% of the Brazilian population, identifying the factors that most influence the efficiency of the sector in the years 2008 and 2016. The M-DEA methodology is an extension of Data Envelopment Analysis (DEA) with multiple DEA executions considering all combinations of inputs and outputs to calculate efficiency scores. The methodology reduces possible biases in the selection of resources and products of the model, ability to support decision-making in favor of improvements in the sector′s efficiency based on national regulatory framework. The analyses show that the companies analyzed can increase their operating results and attendance coverage by more than 60%, given the current levels of infrastructure, human and financial resources in the sector. Based on the simulation of potential efficiency gains in Brazilian basic sanitation companies, the estimates show that the coverage of the population with access to sanitary sewage would go from the current 59.9% to 76.5%. The evidence found provides indications to subsidize sanitation management in the country at the micro-analytical level, enabling a better competitive position in the sector for the integrated management of basic sanitation and its universalization in Brazil.

An optimistic-pessimistic DEA model based on game cross efficiency approach

2020 ◽  
Vol 54 (4) ◽  
pp. 1215-1230
Author(s):  
Mediha Örkcü ◽  
Volkan Soner Özsoy ◽  
H. Hasan Örkcü
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Equilibrium Solution ◽  
Data Envelopment ◽  
Optimal Weights ◽  
Discriminating Power ◽  
Proposed Model ◽  
Essential Problem ◽  
Decision Making Units ◽  
Selection Of

The ranking of the decision making units (DMUs) is an essential problem in data envelopment analysis (DEA). Numerous approaches have been proposed for fully ranking of units. Majority of these methods consider DMUs with optimistic approach, whereas their weaknesses are ignored. In this study, for fully ranking of the units, a modified optimistic–pessimistic approach, which is based on game cross efficiency idea is proposed. The proposed game like iterative optimistic-pessimistic DEA procedure calculates the efficiency scores according to weaknesses and strengths of units and is based on non-cooperative game. This study extends the optimistic-pessimistic DEA approach to obtain robust rank values for DMUs. The proposed approach yields Nash equilibrium solution, thus overcomes the problem of non-uniqueness of the DEA optimal weights that can possibly reduce the usefulness of cross efficiency. Finally, in order to verify the validity of the proposed model and to show the practicability of algorithm, we apply a real-world example for selection of industrial R&D projects. The proposed model can increase the discriminating power of DMUs and can fully rank the DMUs.

scholarly journals STEPWISE SELECTION OF VARIABLES IN DEA USING CONTRIBUTION LOADS

2018 ◽  
Vol 38 (1) ◽  
pp. 31-52 ◽  
Author(s):  
Fernando Fernandez-Palacin ◽  
Maria Auxiliadora Lopez-Sanchez ◽  
Manuel Munõz-Márquez
Keyword(s):  
Stepwise Selection ◽  
Selection Of Variables ◽  
Selection Of

scholarly journals New product concept selection: an integrated approach using data envelopment analysis (DEA) and conjoint analysis (CA)

2014 ◽  
Vol 3 (1) ◽  
pp. 44 ◽  
Author(s):  
SaEd M. Salhieh ◽  
Mira Y. Al-Harris
Keyword(s):  
Data Envelopment Analysis ◽  
Conjoint Analysis ◽  
Perceived Value ◽  
New Product ◽  
Data Envelopment ◽  
Product Concept ◽  
Customer Perceived Value ◽  
Using Data ◽  
Product Concepts ◽  
Selection Of

New product concept development is considered to be a critical step and the main determinant for the success or failure of new product development. This paper introduces a new methodology for the evaluation and selection of new product concepts using Data Envelopment Analysis (DEA) and Conjoint Analysis (CA). The proposed methodology integrates customer perceived value of the new product concepts through the use of CA and uses this perceived value as a measure for the new concepts performance. In addition, the methodology takes into account the development burden that a company has to perform to bring the new concept into a state of market readiness. This development burden is estimated by determining two main factors, namely the burden to produce and the burden to sell the new product concept. The customer perceived value and the development burden are both used in DEA to evaluate the new product concepts resulting in the selection of the best product concept. The applicability of the proposed methodology is illustrated through a case study. Keywords: Product development, concept selection, data envelopment analysis, conjoint analysis.

THE SELECTION OF PROJECTS WITHIN CONSTRAINED RESOURCES USING STOCHASTIC DATA ENVELOPMENT ANALYSIS

2018 ◽  
Vol 52 (3) ◽  
pp. 171-201
Author(s):  
Maliheh Piri ◽  
Farhad Hosseinzadeh Lotfi ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohammad Hasan Behzadi
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Stochastic Data ◽  
Selection Of
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