IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v250y2017i1d10.1007_s10479-015-1980-8.html
   My bibliography  Save this article

On the use of super-efficiency procedures for ranking efficient units and identifying outliers

Author

Listed:
  • Rajiv D. Banker

    (Temple University)

  • Hsihui Chang

    (Drexel University)

  • Zhiqiang Zheng

    (University of Texas at Dallas)

Abstract

Prior research (Banker and Chang in Eur J Oper Res 175: 1311–1320, 2006) has found that super-efficiency based procedures are effective in identifying outliers, but not in ranking efficient units. In this paper, we investigate why the procedures failed to rank efficient units satisfactorily and examine the performance of super-efficiency procedures in different “regions” of production set. We find that the unsatisfactory results mainly originate from the “left corners” of DMUs, those units with relatively smaller values. We further examine the effect of different noise levels on outlier detection using the BG procedure (Banker and Gifford in A relative efficiency model for the evaluation of public health nurse productivity. Mimeo, Carnegie Mellon University, Pittsburgh, 1988). Our results show that the BG procedure is more effective when the noise level is high. We conduct extensive simulation experiments by considering different production functions (Cobb–Douglas and Polynomial), different DEA formulations (BCC and CCR) and different returns-to-scale (CRS and NIRS) assumptions. Our simulation results confirm that the findings in Banker and Chang (2006) are robust under different DEA formulations, production functions and returns to scale assumptions.

Suggested Citation

  • Rajiv D. Banker & Hsihui Chang & Zhiqiang Zheng, 2017. "On the use of super-efficiency procedures for ranking efficient units and identifying outliers," Annals of Operations Research, Springer, vol. 250(1), pages 21-35, March.
  • Handle: RePEc:spr:annopr:v:250:y:2017:i:1:d:10.1007_s10479-015-1980-8
    DOI: 10.1007/s10479-015-1980-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-015-1980-8
    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/s10479-015-1980-8?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. Banker, Rajiv D. & Chang, Hsihui, 2006. "The super-efficiency procedure for outlier identification, not for ranking efficient units," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1311-1320, December.
    2. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    3. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    4. 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.
    5. Charnes, A. & Neralic, L., 1990. "Sensitivity analysis of the additive model in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 48(3), pages 332-341, October.
    6. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Vladimír Holý & Karel Šafr, 2018. "Are economically advanced countries more efficient in basic and applied research?," 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 933-950, December.
    2. Panagiotis Mitropoulos & Alexandros Mitropoulos, 2023. "Evaluating efficiency and technology gaps of the national systems of entrepreneurship using stochastic DEA and club convergence," Operational Research, Springer, vol. 23(1), pages 1-28, March.
    3. Zervopoulos, Panagiotis & Emrouznejad, Ali & Sklavos, Sokratis, 2019. "A Bayesian approach for correcting bias of data envelopment analysis estimators," MPRA Paper 91886, University Library of Munich, Germany.
    4. Ali Tosyali & Jinho Kim & Jeongsub Choi & Yunyi Kang & Myong K. Jeong, 2020. "New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks," Annals of Operations Research, Springer, vol. 288(1), pages 457-474, May.
    5. Martin Bod’a & Martin Dlouhý & Emília Zimková, 2018. "Unobservable or omitted production variables in data envelopment analysis through unit-specific production trade-offs," 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 813-846, December.
    6. Pontus Mattsson & Jonas Månsson & Christian Andersson & Fredrik Bonander, 2018. "A bootstrapped Malmquist index applied to Swedish district courts," European Journal of Law and Economics, Springer, vol. 46(1), pages 109-139, August.
    7. Li, Jiaxin & Peng, Jiachao & Shuai, Chuanmin & Wang, Zihan & Huang, Fubin & Khayyam, Muhammad, 2022. "Does the solar PV program enhance the social empowerment of China's rural poor?," Energy, Elsevier, vol. 253(C).
    8. Khezrimotlagh, Dariush, 2022. "Simulation designs for production frontiers," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1321-1334.
    9. Mostafa Davtalab-Olyaie & Hadis Mahmudi-Baram & Masoud Asgharian, 2023. "Measuring individual efficiency and unit influence in centrally managed systems," Annals of Operations Research, Springer, vol. 321(1), pages 139-164, February.
    10. Panagiotis Mitropoulos & Panagiotis D. Zervopoulos & Ioannis Mitropoulos, 2020. "Measuring performance in the presence of noisy data with targeted desirable levels: evidence from healthcare units," Annals of Operations Research, Springer, vol. 294(1), pages 537-566, November.
    11. Chen, Xin & Zhou, Wenjia, 2023. "Performance evaluation of aquavoltaics in China: Retrospect and prospect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    12. Qiwei Xie & Linda L. Zhang & Haichao Shang & Ali Emrouznejad & Yongjun Li, 2021. "Evaluating performance of super-efficiency models in ranking efficient decision-making units based on Monte Carlo simulations," Annals of Operations Research, Springer, vol. 305(1), pages 273-323, October.
    13. Khezrimotlagh, Dariush & Cook, Wade D. & Zhu, Joe, 2020. "A nonparametric framework to detect outliers in estimating production frontiers," European Journal of Operational Research, Elsevier, vol. 286(1), pages 375-388.
    14. Mahinda Wijesiri & Almudena Martínez-Campillo & Peter Wanke, 2019. "Is there a trade-off between social and financial performance of public commercial banks in India? A multi-activity DEA model with shared inputs and undesirable outputs," Review of Managerial Science, Springer, vol. 13(2), pages 417-442, April.
    15. Arnab Adhikari & Adrija Majumdar & Gaurav Gupta & Arnab Bisi, 2020. "An innovative super-efficiency data envelopment analysis, semi-variance, and Shannon-entropy-based methodology for player selection: evidence from cricket," Annals of Operations Research, Springer, vol. 284(1), pages 1-32, January.

    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. Zervopoulos, Panagiotis & Emrouznejad, Ali & Sklavos, Sokratis, 2019. "A Bayesian approach for correcting bias of data envelopment analysis estimators," MPRA Paper 91886, University Library of Munich, Germany.
    2. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
    3. Banker, Rajiv D. & Chang, Hsihui, 2006. "The super-efficiency procedure for outlier identification, not for ranking efficient units," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1311-1320, December.
    4. Reuben Elan & Verma Bharat Bhushan & Bhat Ramesh, 2001. "Hospital Efficiency: An Empirical Analysis of District and Grant-in-Aid Hospitals in Gujarat," IIMA Working Papers WP2001-07-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    6. Zhu, Joe, 2001. "Super-efficiency and DEA sensitivity analysis," European Journal of Operational Research, Elsevier, vol. 129(2), pages 443-455, March.
    7. Angeliki Flokou & Vassilis Aletras & Dimitris Niakas, 2017. "Decomposition of potential efficiency gains from hospital mergers in Greece," Health Care Management Science, Springer, vol. 20(4), pages 467-484, December.
    8. Agrell, Per J. & Brea-Solís, Humberto, 2017. "Capturing heterogeneity in electricity distribution operations: A critical review of latent class modelling," Energy Policy, Elsevier, vol. 104(C), pages 361-372.
    9. Tran, Trung Hieu & Mao, Yong & Nathanail, Paul & Siebers, Peer-Olaf & Robinson, Darren, 2019. "Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis," Omega, Elsevier, vol. 85(C), pages 156-165.
    10. Bozec, Richard & Dia, Mohamed, 2007. "Board structure and firm technical efficiency: Evidence from Canadian state-owned enterprises," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1734-1750, March.
    11. Sowlati, Taraneh & Paradi, Joseph C., 2004. "Establishing the "practical frontier" in data envelopment analysis," Omega, Elsevier, vol. 32(4), pages 261-272, August.
    12. Guo-Ya Gan & Hsuan-Shih Lee, 2021. "Resolving the infeasibility of the super-efficiency DEA based on DDF," Annals of Operations Research, Springer, vol. 307(1), pages 139-152, December.
    13. Kuosmanen, Timo & Post, Thierry & Scholtes, Stefan, 2007. "Non-parametric tests of productive efficiency with errors-in-variables," Journal of Econometrics, Elsevier, vol. 136(1), pages 131-162, January.
    14. Khezrimotlagh, Dariush & Cook, Wade D. & Zhu, Joe, 2020. "A nonparametric framework to detect outliers in estimating production frontiers," European Journal of Operational Research, Elsevier, vol. 286(1), pages 375-388.
    15. Kyuseok Lee & Kyuwan Choi, 2010. "Cross redundancy and sensitivity in DEA models," Journal of Productivity Analysis, Springer, vol. 34(2), pages 151-165, October.
    16. Dariush Khezrimotlagh & Wade D. Cook & Joe Zhu, 2021. "Number of performance measures versus number of decision making units in DEA," Annals of Operations Research, Springer, vol. 303(1), pages 529-562, August.
    17. Marijana Petrović & Nataša Bojković & Mladen Stamenković, 2018. "A Dea-Based Tool For Tracking Best Practice Exemplars: The Case Of Telecommunications In Ebrd Countries," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 63(218), pages 105-128, July – Se.
    18. Ghasemi, Mohammad Reza & Ignatius, Joshua & Rezaee, Babak, 2019. "Improving discriminating power in data envelopment models based on deviation variables framework," European Journal of Operational Research, Elsevier, vol. 278(2), pages 442-447.
    19. Panayotis Alexakis & Ioannis Tsolas, 2011. "Appraisal of Mutual Equity Fund Performance Using Data Envelopment Analysis," Multinational Finance Journal, Multinational Finance Journal, vol. 15(3-4), pages 273-296, September.
    20. Hibiki, Norio & Sueyoshi, Toshiyuki, 1999. "DEA sensitivity analysis by changing a reference set: regional contribution to Japanese industrial development," Omega, Elsevier, vol. 27(2), pages 139-153, April.

    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:annopr:v:250:y:2017:i:1:d:10.1007_s10479-015-1980-8. 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.