Data Envelopment Analysis (DEA) is becoming an increasingly popular management tool. Questions about it have cropped up recently in the sci.op-research newsgroup but nothing underlines this fact more than the fact that it was featured in a recent issue of Fortune magazine (10/31/94, p.38). Groups such as the Productivity Analysis Research Network (PARN ) and others have supported DEA and other techniques but there hasn’t been a central WWW link. This WWW page is meant to be a basic introduction to DEA and link to other WWW resources. This introduction focuses more on gaining an intuitive understanding of DEA than mathematical rigor because of the varying backgrounds of the readers. Later versions may include more in the way of mathematics and advanced topics in DEA. People already proficient with DEA may find the links to other DEA related Internet sites useful. Eventually I may also add a calendar of events related to DEA.

References in zbMATH (referenced in 241 articles , 1 standard article )

Showing results 1 to 20 of 241.
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  1. Balk, Bert M.; Barbero, Javier; Zofío, José L.: A toolbox for calculating and decomposing total factor productivity indices (2020)
  2. Duarte, Leonardo Tomazeli; Mussio, Alex Pincelli; Torezzan, Cristiano: Dealing with missing information in data envelopment analysis by means of low-rank matrix completion (2020)
  3. Jin, Qianying; Kerstens, Kristiaan; van de Woestyne, Ignace: Metafrontier productivity indices: questioning the common convexification strategy (2020)
  4. Liesiö, Juuso; Andelmin, Juho; Salo, Ahti: Efficient allocation of resources to a portfolio of decision making units (2020)
  5. Pereira, Miguel Alves; Figueira, José Rui; Marques, Rui Cunha: Using a Choquet integral-based approach for incorporating decision-maker’s preference judgments in a data envelopment analysis model (2020)
  6. Simar, Léopold; Wilson, Paul W.: Technical, allocative and overall efficiency: estimation and inference (2020)
  7. Zelenyuk, Valentin: Aggregation of inputs and outputs prior to data envelopment analysis under big data (2020)
  8. Neralić, Luka; Wendell, Richard E.: Sensitivity in DEA: an algorithmic approach (2019)
  9. Puggioni, Daniela; Stefanou, Spiro E.: The value of being socially responsible: a primal-dual approach (2019)
  10. Simar, Léopold; W. Wilson, Paul: Central limit theorems and inference for sources of productivity change measured by nonparametric Malmquist indices (2019)
  11. Bogetoft, Peter; Kromann, Lene: Evaluating treatment effects using data envelopment analysis on matched samples: an analysis of electronic information sharing and firm performance (2018)
  12. Briec, Walter; Kerstens, Kristiaan; Prior, Diego; Van de Woestyne, Ignace: Testing general and special Färe-Primont indices: a proposal for public and private sector synthetic indices of European regional expenditures and tourism (2018)
  13. Chu, Jun-Fei; Wu, Jie; Song, Ma-Lin: An SBM-DEA model with parallel computing design for environmental efficiency evaluation in the big data context: a transportation system application (2018)
  14. Du, Juan; Chen, Yao; Huang, Ying: A modified Malmquist-Luenberger productivity index: assessing environmental productivity performance in China (2018)
  15. Fiala, Petr: Project portfolio designing using data envelopment analysis and de novo optimisation (2018)
  16. Fukuyama, Hirofumi; Matousek, Roman: Nerlovian revenue inefficiency in a bank production context: evidence from Shinkin banks (2018)
  17. Pourhabib, Azam; Kordrostami, Sohrab; Matin, Reza Kazemi: Performance measurement in data envelopment analysis without slacks: an application to electricity distribution companies (2018)
  18. Rogge, Nicky: Composite indicators as generalized benefit-of-the-doubt weighted averages (2018)
  19. Zhu, Qingyuan; Wu, Jie; Song, Malin: Efficiency evaluation based on data envelopment analysis in the big data context (2018)
  20. Chen, Wen-Chih; Lai, Sheng-Yung: Determining radial efficiency with a large data set by solving small-size linear programs (2017)

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