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On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes' care planning process

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  • B. J. Gajewski
  • R. Lee
  • M. Bott
  • U. Piamjariyakul
  • R. L. Taunton

Abstract

Data envelopment analysis (DEA) is a deterministic econometric model for calculating efficiency by using data from an observed set of decision-making units (DMUs). We propose a method for calculating the distribution of efficiency scores. Our framework relies on estimating data from an unobserved set of DMUs. The model provides posterior predictive data for the unobserved DMUs to augment the frontier in the DEA that provides a posterior predictive distribution for the efficiency scores. We explore the method on a multiple-input and multiple-output DEA model. The data for the example are from a comprehensive examination of how nursing homes complete a standardized mandatory assessment of residents.

Suggested Citation

  • B. J. Gajewski & R. Lee & M. Bott & U. Piamjariyakul & R. L. Taunton, 2009. "On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes' care planning process," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(9), pages 933-944.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:933-944
    DOI: 10.1080/02664760802552986
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    References listed on IDEAS

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    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. GIJBELS, Irène & MAMMEN, Enno & PARK, Byeong U. & SIMAR, Léopold, 1997. "On estimation of monotone and concave frontier functions," LIDAM Discussion Papers CORE 1997031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    4. Sengupta, Jati K., 1990. "Transformations in stochastic DEA models," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 109-123.
    5. Laurens Cherchye & Thierry Post, 2003. "Methodological Advances in DEA: A survey and an application for the Dutch electricity sector," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(4), pages 410-438, November.
    6. Tsionas, Efthymios G., 2003. "Combining DEA and stochastic frontier models: An empirical Bayes approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 499-510, June.
    7. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, September.
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    Cited by:

    1. Tu D. Q. Le & Thanh Ngo & Tin H. Ho & Dat T. Nguyen, 2022. "ICT as a Key Determinant of Efficiency: A Bootstrap-Censored Quantile Regression (BCQR) Analysis for Vietnamese Banks," IJFS, MDPI, vol. 10(2), pages 1-15, June.
    2. Chris Tofallis, 2024. "Objective Weights for Scoring: The Automatic Democratic Method," Papers 2409.02087, arXiv.org.
    3. Friesner, Daniel & Mittelhammer, Ron & Rosenman, Robert, 2013. "Inferring the incidence of industry inefficiency from DEA estimates," European Journal of Operational Research, Elsevier, vol. 224(2), pages 414-424.

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