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Dergiler / Gazi University Journal of Science / 2026 / Volume: 39 Issue: 2

Using Parametric Bootstrapping for Estimation of the Incidence of Inefficiency: Improving Features of Non-Parametric Bootstrap Estimator

Deniz Özonur(Gazi University)0000-0002-7622-1008
Mehmet Güray Ünsal(BASKENT UNIVERSITY)0000-0001-7081-9060
Sayfa
—
Yayın Tarihi
2026-06-01
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Additionally, it is experimentally demonstrated that the parametric bootstrap based on the Bayesian method applied to a specific posterior distribution converges to the same features as the estimators obtained with the Bayesian estimator based on that posterior distribution.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study proposes parametric bootstrap estimation methods to improve features of the non-parametric bootstrap estimator of Incidence of Inefficiency proposed in the literature.
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Temel Bulgular & Veri:In the simulation study, the Maximum Likelihood-based parametric bootstrap method yields the best results in small sample sizes and a limited number of input and output variable situations.
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Sonuç & Katkı:Additionally, it is experimentally demonstrated that the parametric bootstrap based on the Bayesian method applied to a specific posterior distribution converges to the same features as the estimators obtained with the Bayesian estimator based on that posterior distribution.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Bootstrapping · Estimation · Incidence of inefficiency

Abstract

This study proposes parametric bootstrap estimation methods to improve features of the non-parametric bootstrap estimator of Incidence of Inefficiency proposed in the literature. This study is the first to propose parametric bootstrap estimation methods to improve features of the IOI estimators. In the simulation study, the Maximum Likelihood-based parametric bootstrap method yields the best results in small sample sizes and a limited number of input and output variable situations. However, in cases where the number of input and output variables increases, which reduces the discrimination power of classical Data Envelopment Analysis models, the Bayesian estimator with latent variable adjustment tends to yield better results than the proposed estimators for IOI in the literature. Additionally, it is experimentally demonstrated that the parametric bootstrap based on the Bayesian method applied to a specific posterior distribution converges to the same features as the estimators obtained with the Bayesian estimator based on that posterior distribution.

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Bu Makaleye Atıf Yap

Özonur, D., Ünsal, M. G. (2026). Using Parametric Bootstrapping for Estimation of the Incidence of Inefficiency: Improving Features of Non-Parametric Bootstrap Estimator. Gazi University Journal of Science, Volume: 39 Issue: 2. https://doi.org/10.35378/gujs.1817298

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