IDEAS home Printed from https://ideas.repec.org/a/spr/cejnor/v31y2023i4d10.1007_s10100-023-00847-3.html
   My bibliography  Save this article

Buffered-ranking intervals for virtual profit efficiency analysis

Author

Listed:
  • Yongqiao Wang

    (Zhejiang Gongshang University)

  • He Ni

    (Zhejiang Gongshang University)

  • Stan Uryasev

    (Stony Brook University)

Abstract

The efficiency ranking of a decision making units (DMU) measures its relative position among a group of DMUs over sets of feasible virtual prices that characterize preferences for input and output variables. But the efficiency ranking of a DMU conveys no information about the gap between this DMU and those superior and peer DMUs. So we propose an alternative efficiency measure named buffered-ranking for efficiency analysis. The statement that the efficiency buffered-ranking of a DMU is k implies that its efficiency score reaches the average of the top k efficiency scores of all DMUs. The proposed buffered-ranking is monotone with the conventional ranking, and conveys more information about its relation with superior and peer DMUs. When the efficiency score is based on virtual profit, i.e. the difference between virtual revenue and virtual cost, the calculation of the best buffered-ranking is equivalent to a continuous linear program. We also study the worst buffered-ranking that is opposite to the best buffered-ranking. Experiments demonstrate the advantages of buffered-ranking over conventional ranking.

Suggested Citation

  • Yongqiao Wang & He Ni & Stan Uryasev, 2023. "Buffered-ranking intervals for virtual profit efficiency analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1149-1181, December.
  • Handle: RePEc:spr:cejnor:v:31:y:2023:i:4:d:10.1007_s10100-023-00847-3
    DOI: 10.1007/s10100-023-00847-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10100-023-00847-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10100-023-00847-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Adam, Lukáš & Branda, Martin, 2021. "Risk-aversion in data envelopment analysis models with diversification," Omega, Elsevier, vol. 102(C).
    2. Mafusalov, Alexander & Shapiro, Alexander & Uryasev, Stan, 2018. "Estimation and asymptotics for buffered probability of exceedance," European Journal of Operational Research, Elsevier, vol. 270(3), pages 826-836.
    3. Salo, Ahti A., 1995. "Interactive decision aiding for group decision support," European Journal of Operational Research, Elsevier, vol. 84(1), pages 134-149, July.
    4. R. Färe & S. Grosskopf & G. Whittaker, 2013. "Directional output distance functions: endogenous directions based on exogenous normalization constraints," Journal of Productivity Analysis, Springer, vol. 40(3), pages 267-269, December.
    5. Robert G. Chambers & Rulon D. Pope, 1996. "Aggregate Productivity Measures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(5), pages 1360-1365.
    6. Josef Jablonsky, 2018. "Ranking of countries in sporting events using two-stage data envelopment analysis models: a case of Summer Olympic Games 2016," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 951-966, December.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Galina Besstremyannaya, 2011. "Managerial performance and cost efficiency of Japanese local public hospitals: A latent class stochastic frontier model," Health Economics, John Wiley & Sons, Ltd., vol. 20(S1), pages 19-34, September.
    9. Güray Kara & Ayşe Özmen & Gerhard-Wilhelm Weber, 2019. "Stability advances in robust portfolio optimization under parallelepiped uncertainty," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(1), pages 241-261, March.
    10. Justin R. Davis & Stan Uryasev, 2016. "Analysis of tropical storm damage using buffered probability of exceedance," 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. 83(1), pages 465-483, August.
    11. Danjue Shang & Victor Kuzmenko & Stan Uryasev, 2018. "Cash flow matching with risks controlled by buffered probability of exceedance and conditional value-at-risk," Annals of Operations Research, Springer, vol. 260(1), pages 501-514, January.
    12. Matthew Norton & Valentyn Khokhlov & Stan Uryasev, 2021. "Calculating CVaR and bPOE for common probability distributions with application to portfolio optimization and density estimation," Annals of Operations Research, Springer, vol. 299(1), pages 1281-1315, April.
    13. Galina Besstremyannaya, 2013. "The impact of Japanese hospital financing reform on hospital efficiency: A difference-in-difference approach," The Japanese Economic Review, Japanese Economic Association, vol. 64(3), pages 337-362, September.
    14. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
    15. Martin Branda & Miloš Kopa, 2014. "On relations between DEA-risk models and stochastic dominance efficiency tests," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(1), pages 13-35, March.
    16. Podinovski, Victor V., 2001. "DEA models for the explicit maximisation of relative efficiency," European Journal of Operational Research, Elsevier, vol. 131(3), pages 572-586, June.
    17. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthew Norton & Valentyn Khokhlov & Stan Uryasev, 2021. "Calculating CVaR and bPOE for common probability distributions with application to portfolio optimization and density estimation," Annals of Operations Research, Springer, vol. 299(1), pages 1281-1315, April.
    2. Aparicio, Juan & Ortiz, Lidia & Santín, Daniel, 2021. "Comparing group performance over time through the Luenberger productivity indicator: An application to school ownership in European countries," European Journal of Operational Research, Elsevier, vol. 294(2), pages 651-672.
    3. Galina Besstremyannaya, 2015. "Heterogeneous effect of residency matching and prospective payment on labor returns and hospital scale economies," Discussion Papers 15-001, Stanford Institute for Economic Policy Research.
    4. Branda, Martin, 2015. "Diversification-consistent data envelopment analysis based on directional-distance measures," Omega, Elsevier, vol. 52(C), pages 65-76.
    5. Kerstens, Kristiaan & Mazza, Paolo & Ren, Tiantian & Van de Woestyne, Ignace, 2022. "Multi-Time and Multi-Moment Nonparametric Frontier-Based Fund Rating: Proposal and Buy-and-Hold Backtesting Strategy," Omega, Elsevier, vol. 113(C).
    6. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    7. Martin Branda, 2016. "Mean-value at risk portfolio efficiency: approaches based on data envelopment analysis models with negative data and their empirical behaviour," 4OR, Springer, vol. 14(1), pages 77-99, March.
    8. Pertaia, Giorgi & Prokhorov, Artem & Uryasev, Stan, 2022. "A new approach to credit ratings," Journal of Banking & Finance, Elsevier, vol. 140(C).
    9. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, New Economic School (NES).
    10. Shawna Grosskopf & Kathy Hayes & Lori L. Taylor, 2014. "Applied efficiency analysis in education," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 19-26.
    11. Ahti Salo & Antti Punkka, 2011. "Ranking Intervals and Dominance Relations for Ratio-Based Efficiency Analysis," Management Science, INFORMS, vol. 57(1), pages 200-214, January.
    12. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    13. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    14. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    15. Murinde, Victor & Zhao, Tianshu, 2009. "Bank competition, risk taking and productive efficiency: Evidence from Nigeria's banking reform experiments," Stirling Economics Discussion Papers 2009-23, University of Stirling, Division of Economics.
    16. Soteriou, Andreas C. & Zenios, Stavros A., 1999. "Using data envelopment analysis for costing bank products," European Journal of Operational Research, Elsevier, vol. 114(2), pages 234-248, April.
    17. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
    18. Ruiz, Jose L. & Sirvent, Inmaculada, 2001. "Techniques for the assessment of influence in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 390-399, July.
    19. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    20. Tarnaud, Albane Christine & Leleu, Hervé, 2018. "Portfolio analysis with DEA: Prior to choosing a model," Omega, Elsevier, vol. 75(C), pages 57-76.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:cejnor:v:31:y:2023:i:4:d:10.1007_s10100-023-00847-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.