IDEAS home Printed from https://ideas.repec.org/r/inm/oropre/v58y2010i6p1743-1754.html
   My bibliography  Save this item

Differential Characteristics of Efficient Frontiers in Data Envelopment Analysis

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Victor V. Podinovski & Ole Bent Olesen & Cláudia S. Sarrico, 2018. "Nonparametric Production Technologies with Multiple Component Processes," Operations Research, INFORMS, vol. 66(1), pages 282-300, January.
  2. Podinovski, Victor V., 2016. "Optimal weights in DEA models with weight restrictions," European Journal of Operational Research, Elsevier, vol. 254(3), pages 916-924.
  3. Lee, Chia-Yen & Charles, Vincent, 2022. "A robust capacity expansion integrating the perspectives of marginal productivity and capacity regret," European Journal of Operational Research, Elsevier, vol. 296(2), pages 557-569.
  4. Chia-Yen Lee, 2017. "Directional marginal productivity: a foundation of meta-data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 544-555, May.
  5. Noah J Miller & Jason S Bergtold & Allen M Featherstone, 2019. "Economic elasticities of input substitution using data envelopment analysis," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-15, August.
  6. Lee, Chia-Yen & Johnson, Andrew L., 2014. "Proactive data envelopment analysis: Effective production and capacity expansion in stochastic environments," European Journal of Operational Research, Elsevier, vol. 232(3), pages 537-548.
  7. Ke Wang & Yujiao Xian & Chia-Yen Lee & Yi-Ming Wei & Zhimin Huang, 2019. "On selecting directions for directional distance functions in a non-parametric framework: a review," Annals of Operations Research, Springer, vol. 278(1), pages 43-76, July.
  8. Sahoo, Biresh K. & Tone, Kaoru, 2013. "Non-parametric measurement of economies of scale and scope in non-competitive environment with price uncertainty," Omega, Elsevier, vol. 41(1), pages 97-111.
  9. Victor V. Podinovski & Robert G. Chambers & Kazim Baris Atici & Iryna D. Deineko, 2016. "Marginal Values and Returns to Scale for Nonparametric Production Frontiers," Operations Research, INFORMS, vol. 64(1), pages 236-250, February.
  10. Liangpeng Wu & Qingyuan Zhu, 2021. "Impacts of the carbon emission trading system on China’s carbon emission peak: a new data-driven approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2487-2515, July.
  11. Sahoo, Biresh K & Khoveyni, Mohammad & Eslami, Robabeh & Chaudhury, Pradipta, 2016. "Returns to scale and most productive scale size in DEA with negative data," European Journal of Operational Research, Elsevier, vol. 255(2), pages 545-558.
  12. Lee, Chia-Yen, 2014. "Meta-data envelopment analysis: Finding a direction towards marginal profit maximization," European Journal of Operational Research, Elsevier, vol. 237(1), pages 207-216.
  13. Hennebel, Veerle & Simper, Richard & Verschelde, Marijn, 2017. "Is there a prison size dilemma? An empirical analysis of output-specific economies of scale," European Journal of Operational Research, Elsevier, vol. 262(1), pages 306-321.
  14. Atici, Kazim Baris & Podinovski, Victor V., 2015. "Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture," Omega, Elsevier, vol. 54(C), pages 72-83.
  15. Wei, Xiao & Zhang, Ning, 2020. "The shadow prices of CO2 and SO2 for Chinese Coal-fired Power Plants: A partial frontier approach," Energy Economics, Elsevier, vol. 85(C).
  16. Peixin Duan, 2022. "How large of a grant size is appropriate? Evidence from the National Natural Science Foundation of China," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-14, February.
  17. Panagiotis Ravanos & Giannis Karagiannis, 2022. "On VEA, Production Trade-offs and Weight Restrictions," Discussion Paper Series 2022_04, Department of Economics, University of Macedonia, revised Jun 2022.
  18. Mehdiloo, Mahmood & Podinovski, Victor V., 2019. "Selective strong and weak disposability in efficiency analysis," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1154-1169.
  19. Seda Busra Sarac & Kazim Baris Atici & Aydin Ulucan, 2022. "Elasticity measurement on multiple levels of DEA frontiers: an application to agriculture," Journal of Productivity Analysis, Springer, vol. 57(3), pages 313-324, June.
  20. Atici, Kazim Baris & Podinovski, Victor V., 2012. "Mixed partial elasticities in constant returns-to-scale production technologies," European Journal of Operational Research, Elsevier, vol. 220(1), pages 262-269.
  21. Vladimir Krivonozhko & Finn Førsund & Andrey Lychev, 2012. "Returns-to-scale properties in DEA models: the fundamental role of interior points," Journal of Productivity Analysis, Springer, vol. 38(2), pages 121-130, October.
  22. Podinovski, Victor V., 2017. "Returns to scale in convex production technologies," European Journal of Operational Research, Elsevier, vol. 258(3), pages 970-982.
  23. Stefan Seifert, 2016. "Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation," Discussion Papers of DIW Berlin 1571, DIW Berlin, German Institute for Economic Research.
  24. Podinovski, Victor V., 2019. "Direct estimation of marginal characteristics of nonparametric production frontiers in the presence of undesirable outputs," European Journal of Operational Research, Elsevier, vol. 279(1), pages 258-276.
  25. Victor V. Podinovski & Tatiana Bouzdine-Chameeva, 2013. "Weight Restrictions and Free Production in Data Envelopment Analysis," Operations Research, INFORMS, vol. 61(2), pages 426-437, April.
  26. Afsharian, Mohsen & Podinovski, Victor V., 2018. "A linear programming approach to efficiency evaluation in nonconvex metatechnologies," European Journal of Operational Research, Elsevier, vol. 268(1), pages 268-280.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.