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Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach.

Chunhua Chen1,2, Jianwei Ren3,4,5, Lijun Tang5

  • 1School of Business Administration, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China.

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Summary
This summary is machine-generated.

This study introduces a multi-criteria evaluation approach for data envelopment analysis (DEA) with missing data. The method estimates efficiency bounds and relative efficiency, outperforming traditional techniques.

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Area of Science:

  • Operations Research
  • Management Science
  • Econometrics

Background:

  • Traditional Data Envelopment Analysis (DEA) models require complete data, which is often unavailable in real-world scenarios.
  • Existing methods for handling missing data in DEA have limitations and drawbacks.
  • Missing data poses a significant challenge in accurately measuring the efficiency of decision-making units (DMUs).

Purpose of the Study:

  • To propose a novel multi-criteria evaluation approach for assessing DMU efficiency when data is incomplete.
  • To develop robust methods for estimating efficiency bounds and relative efficiency in the presence of missing data.
  • To provide a comprehensive framework for selecting the most appropriate efficiency scenario.

Main Methods:

  • Estimating upper and lower bounds of DMU efficiency using interval additive integer-valued DEA models with undesirable outputs (I-addIDEA-U).
  • Evaluating relative efficiency via the "Halo + Hot deck" DEA method or regression DEA techniques based on data correlation.
  • Applying a multi-index comprehensive evaluation method to determine the final efficiency scenario.

Main Results:

  • The proposed multi-criteria approach effectively handles missing data in DEA.
  • The I-addIDEA-U models accommodate integer-valued variables and undesirable outputs.
  • The case study demonstrates the superiority of the proposed method over traditional approaches like mean imputation, deletion, and dummy entries.

Conclusions:

  • The developed multi-criteria evaluation approach offers a more effective solution for DEA with missing data.
  • This method enhances the accuracy and reliability of efficiency measurements in practical applications.
  • The approach provides a flexible framework for addressing various data conditions in efficiency analysis.