Estimation of the incidence of inefficiency using bootstrap and Bayesian estimators

Unsal M. G., Friesner D., Rosenman R. E., ÖRKCÜ M.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/03610918.2024.2423221
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Gazi Üniversitesi Adresli: Evet

Özet

This paper compares estimates from nonparametric bootstrapping to Bayesian methods for the incidence of inefficiency (IOI) from Data Envelopment Analysis when applied to finite populations. We find for extremely simple production technologies (one input, one output, and a single ray production technology) with large sample sizes, nonparametric bootstrapping yields better estimates of the IOI compared to Bayesian methods that do and do not account for the latent aspect of the true IOI. As the production process becomes more complex, Bayesian methods, especially those that account for a latent IOI, outperform nonparametric bootstrapping methods. Our conclusion is that Bayesian methods are superior for estimating the IOI.