A computational analysis of the impact of correlation and data translation on DEA efficiency scores

References

• Adler, N. and J. Berechman, “Measuring airport quality from the airlines’ viewpoint: An application of data envelopment analysis,” Transport Policy, 8, 171–181 (2001).10.1016/S0967-070X(01)00011-7
   Google Scholar
• Ali, A. I. and L. M. Seiford, “Translation invariance in data envelopment analysis,” Operations Research Letters, 9, 403–405 (1990).
   Web of Science ®Google Scholar
• Bao, C. P., K. C. Lee and H. L. Pu, “A study of the input/output variable characteristics in DEA,” Journal of the Chinese Institute of Industrial Engineers, 27, 429–437 (2010).10.1080/10170669.2010.525393
   Google Scholar
• Bogetoft, P., “DEA-based yardstick competition: The optimality of best practice regulation,” Annals of Operations Research, 73, 277–298 (1997).10.1023/A:1018985313272
   Web of Science ®Google Scholar
• Cooper, W. W., K. S. Park and J. T. Pastor, “RAM: A range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA,” Journal of Productivity Analysis, 11, 5–42 (1999).10.1023/A:1007701304281
   Web of Science ®Google Scholar
• Cooper, W. W., L. M. Seiford and J. Zhu, “Data envelopment analysis: History, models, and interpretations,” in Cooper, Seiford and Zhu, editors Handbook on Data Envelopment Analysis; International Series in Operations Research and Management Science, Springer US, 2011, p. 1–39.10.1007/978-1-4419-6151-8
   Google Scholar
• Dulá, J. H., “A computational study of DEA with massive data sets,” Computers and Operations Research, 35, 1191–1203 (2008).10.1016/j.cor.2006.07.011
   Web of Science ®Google Scholar
• Dyson, R. G., R. Allen, A. S. Camanho, V. V. Podinovski, C. S. Sarrico and E. A. Shale, “Pitfalls and protocols in DEA,” European Journal of Operational Research, 132, 245–259 (2001).10.1016/S0377-2217(00)00149-1
   Web of Science ®Google Scholar
• Jenkins, L. and M. Anderson, “A multivariate statistical approach to reducing the number of variables in data envelopment analysis,” European Journal of Operational Research, 147, 51–61 (2003).10.1016/S0377-2217(02)00243-6
   Web of Science ®Google Scholar
• Kuo, C. C. and W. C. Chen, “An objective-free outlying point detection model in data envelopment analysis,” Journal of the Chinese Institute of Industrial Engineers, 27, 294–303 (2010).10.1080/10170669.2010.482267
   Google Scholar
• Lee, K. and K. Choi, “Cross redundancy and sensitivity in DEA models,” Journal of Productivity Analysis, 34, 151–165 (2010).10.1007/s11123-009-0166-2
   Web of Science ®Google Scholar
• Liu, C. M., H. S. Hsu, S. T. Wang and H. K. Lee, “A performance evaluation model based on AHP and DEA,” Journal of the Chinese Institute of Industrial Engineers, 22, 243–251 (2005).10.1080/10170660509509294
   Google Scholar
• López, F. J., “Generalizing cross redundancy in data envelopment analysis,” European Journal of Operational Research, 214, 716–721 (2011).10.1016/j.ejor.2011.05.020
   Web of Science ®Google Scholar
• López, F. J. and J. H. Dulá, “Adding and removing an attribute in a DEA model: Theory and processing,” Journal of the Operational Research Society, 59, 1674–1684 (2008).10.1057/palgrave.jors.2602505
   Web of Science ®Google Scholar
• Lovell, C. A. K. and J. Pastor, “Units invariant and translation invariant DEA models,” Operations Research Letters, 18, 147–151 (1995).10.1016/0167-6377(95)00044-5
   Web of Science ®Google Scholar
• Pastor, J., “Chapter 3 translation invariance in data envelopment analysis: A generalization,” Annals of Operations Research, 66, 91–102 (1996).10.1007/BF02187295
   Google Scholar
• Pedraja-Chaparro, F., J. Salinas-Jiménez and P. Smith, “On the quality of the data envelopment analysis model,” Journal of the Operational Research Society, 50, 636–644 (1999).10.1057/palgrave.jors.2600741
   Web of Science ®Google Scholar
• Portela, M. C. A. S., E. Thanassoulis and G. P. M. Simpson, “Negative data in DEA: A directional distance approach applied to bank branches,” Journal of the Operational Research Society, 55, 1111–1121 (2004).10.1057/palgrave.jors.2601768
   Web of Science ®Google Scholar
• Sharp, J. A., W. Meng and W. Liu, “A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs,” Journal of the Operational Research Society, 58, 1672–1677 (2007).10.1057/palgrave.jors.2602318
   Web of Science ®Google Scholar
• Simar, L. and P. W. Wilson, “Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models,” Management Science, 44, 49–61 (1998).10.1287/mnsc.44.1.49
   Web of Science ®Google Scholar
• Simar, L. and P. W. Wilson, “Non-parametric tests of returns to scale,” European Journal of Operational Research, 139, 115–132 (2002).10.1016/S0377-2217(01)00167-9
   Web of Science ®Google Scholar
• Sitmo, Generating correlated random numbers, Available online at: www.sitmo.com/article/generating-correlated-random-numbers/ (accessed June 8, 2015).
   Google Scholar
• Smith, P., “Model misspecification in data envelopment analysis,” Annals of Operations Research, 73, 233–252 (1997).10.1023/A:1018981212364
   Web of Science ®Google Scholar
• Sueyoshi, T. and T. Honma, “DEA network computing in multi-stage parallel processes,” International Transactions in Operational Research, 10, 217–244 (2003).10.1111/itor.2003.10.issue-3
   Google Scholar
• Viswanathan, M, Cholesky factorization and Matlab code, Available online at: http://www.gaussianwaves.com/2013/05/cholesky-factorization-and-matlab-code/ (accessed June 8, 2015).
   Google Scholar
• Wesley, Simulating random multivatiate correlated data (continuous variables), Available online at: www.r-bloggers.com/simulating-random-multivariate-correlated-data-continuous-variables/ (accessed June 8, 2015).
   Google Scholar