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Forest-Based Resampling for Confidence Interval Estimation of Efficiencies in Data Envelopment Analysis

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  • Yu Zhao

    (Tokyo University of Science)

Abstract

Standard Data Envelopment Analysis (DEA) models are deterministic, and previous studies have made numerous efforts to introduce statistical analysis into DEA, such as the bootstrapping algorithm and regression-based approaches. In this study, we consider probabilistic variations present in input-output vectors and propose a forest-based sampling procedure to handle the statistical properties of efficiencies across different orientations of the DEA model. To capture the probability distributions of the data, we classify observed decision-making units into several clusters and maximize the information gain of each cluster using a Gaussian-based entropy function. The proposed approach is illustrated using a data set used in previous studies.

Suggested Citation

  • Yu Zhao, 2025. "Forest-Based Resampling for Confidence Interval Estimation of Efficiencies in Data Envelopment Analysis," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-98177-7_6
    DOI: 10.1007/978-3-031-98177-7_6
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