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

Yu Zhao ()
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Yu Zhao: Tokyo University of Science

A chapter in Advances in the Theory and Practice of Data Envelopment Analysis, 2025, pp 62-76 from Springer

Abstract: 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.

Keywords: Data Envelopment Analysis (DEA); Forest-Based Resampling; Confidence Interval; Gaussian-based entropy (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-98177-7_6

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DOI: 10.1007/978-3-031-98177-7_6

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