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Random effects logistic regression model for ranking efficiency in data envelopment analysis

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  • S Y Sohn

    (Yonsei University)

Abstract

Ranking efficiency based on data envelopment analysis (DEA) results can be used for grouping decision-making units (DMUs). The resulting group membership can be partly related to the environmental characteristics of DMU, which are not used either as input or output. Utilizing the expert knowledge on super efficiency DEA results, we propose a multinomial Dirichlet regression model, which can be used for the purpose of selection of new projects. A case study is presented in the context of ranking analysis of new information technology commercialization projects. It is expected that our proposed approach can complement the DEA ranking results with environmental factors and at the same time it facilitates the prediction of efficiency of new DMUs with only given environmental characteristics.

Suggested Citation

  • S Y Sohn, 2006. "Random effects logistic regression model for ranking efficiency in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1289-1299, November.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:11:d:10.1057_palgrave.jors.2602117
    DOI: 10.1057/palgrave.jors.2602117
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    References listed on IDEAS

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    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
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    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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    5. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
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    Citations

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    Cited by:

    1. Daniel Friesner & Ron Mittelhammer & Robert Rosenmane, 2006. "Inferring the Latent Incidence of Inefficiency from DEA Estimates and Bayesian Priors," Working Papers 2006-8, School of Economic Sciences, Washington State University.
    2. Epure, Mircea & Kerstens, Kristiaan & Prior, Diego, 2011. "Bank productivity and performance groups: A decomposition approach based upon the Luenberger productivity indicator," European Journal of Operational Research, Elsevier, vol. 211(3), pages 630-641, June.
    3. Nima Mirzaei & Béla Vizvári, 2015. "A New Approach to Reconstruction of Moody’s Rating System for Countries Investment Risk Rating," Journal of Empirical Economics, Research Academy of Social Sciences, vol. 4(3), pages 167-182.
    4. S Y Sohn & Y Kim & B T Kim, 2009. "Cost of ownership model for spare engines purchase for the Korean navy acquisition program," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1674-1682, December.
    5. Sohn, So Young & Kim, Hong Sik, 2007. "Random effects logistic regression model for default prediction of technology credit guarantee fund," European Journal of Operational Research, Elsevier, vol. 183(1), pages 472-478, November.
    6. Kim, Yoonseong & Sohn, So Young, 2008. "Random effects model for credit rating transitions," European Journal of Operational Research, Elsevier, vol. 184(2), pages 561-573, January.
    7. Seyed Ali Rakhshan, 2017. "Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(8), pages 906-918, August.
    8. Eliane Gomes & João Soares de Mello & Geraldo Souza & Lidia Angulo Meza & João Mangabeira, 2009. "Efficiency and sustainability assessment for a group of farmers in the Brazilian Amazon," Annals of Operations Research, Springer, vol. 169(1), pages 167-181, July.
    9. 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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