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Data Envelopment Analysis With Missing Data: A Multiple Linear Regression Analysis Approach

Author

Listed:
  • YA CHEN

    (School of Management, University of Science and Technology of China, Hefei, Anhui Province, 230026, P. R. China)

  • YONGJUN LI

    (School of Management, University of Science and Technology of China, Hefei, Anhui Province, 230026, P. R. China)

  • HUAQING WU

    (School of Economics, Hefei University of Technology, Hefei, Anhui Province, 230009, P. R. China)

  • LIANG LIANG

    (School of Management, University of Science and Technology of China, Hefei, Anhui Province, 230026, P. R. China)

Abstract

Data envelopment analysis (DEA) assumes that the data set is precise when performing efficiency evaluation of peer decision making units (DMUs). The current paper proposes a multiple linear regression analysis (MLRA) approach to estimate missing values if some of the entries in the data set are missing. Its algorithm to derive the estimations is also proposed. In order to verify the credibility of the proposed approach, an example of 30 US commercial banks is applied to case analysis. Using the proposed algorithm, the efficiencies of all DMUs are obtained. A Friedman test and a Kendall's Tau rank correlation analysis statistically examine the results. Moreover, the efficiency interval and efficiency distribution for a DMU are obtained considering random errors of the estimations. After that, an example of public secondary schools serves to illustrate the applications in the end.

Suggested Citation

  • Ya Chen & Yongjun Li & Huaqing Wu & Liang Liang, 2014. "Data Envelopment Analysis With Missing Data: A Multiple Linear Regression Analysis Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 137-153.
    • Handle: RePEc:wsi:ijitdm:v:13:y:2014:i:01:n:s0219622014500060
    DOI: 10.1142/S0219622014500060
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    References listed on IDEAS

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    1. Timo Kuosmanen, 2002. "Modeling Blank Data Entries in Data Envelopment Analysis," Econometrics 0210001, University Library of Munich, Germany.
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    Cited by:

    1. Chunhua Chen & Jianwei Ren & Lijun Tang & Haohua Liu, 2020. "Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    2. Zhu, Weiwei & Yu, Yu & Sun, Panpan, 2018. "Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability," European Journal of Operational Research, Elsevier, vol. 269(1), pages 99-110.

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