Esra Öztürk, Hasan Bal
3.437 1.305


In this study , Canonical correlation analysis (CCA)  and Data envelopment analysis (DEA)  techniques are used.  Canonical correlation analysis (CCA) is a multivariate statistical technique that can be used to determine the relationship between two multiple variable sets. Data envelopment analysis (DEA) is a linear programming (LP) technique for measuring the relative efficiency of peer decision making units(DMUs) when multiple inputs and outputs are present. A beneficial method for model selection is proposed in this study. Efficiencies are calculated for all possible DEA model specifications. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The results are analysed using Canonical correlation analysis (CCA).


Canonical Correlation Analysis, Data Envelopment Analysis, Efficiency Evaluation.

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Francisco J. López, Johnny C. Ho & Alex J. Ruiz-Torres (2016) A computational analysis of the impact of correlation and data translation on DEA efficiency scores, Journal of Industrial and Production Engineering, 33:3, 192-204,

Adler, N., Friedman, L. ve Sinuany-Stern, Z. (2002). “Review of ranking methods in the data envelopment analysis context”, European Journal of Operational Research, 140, ss.249–265.

Bagozzi, R. P., Fornell, C. ve Larcker, D. F. (1981). “Canonical Correlation Analysis as a Special Case of a Structural Relations Model”, Multivariate Behavioral Research, 16(4), 437-454.

Banker, R., Charnes, A. ve Cooper, W. W. (1984). “Some Models For Estimating Technical and Scale İnefficiencies in Data Envelopment Analysis,” Management Science, 30, ss.1078–1092.

Eubank,R. L. ve Hsing,T. (2008). “Canonical Correlation for Stochastic Processes”, Stochastic Processes and their Applications Volume 118, Issue 9, ss.1634-1661

Farrell, M. J., (1957), “The Measurement of Productivity Efficiency”, Journal Of the Royal Statistical Society, ss.120:253-290.

Fornell, C. ve Larcker, D. F. (1980). “The Use of Canonical Correlation Analysis in Accounting Research”, The Journal of Business Finance&Accounting, 7(3), ss.455-470.

Hotelling H. (1936), “Relations Between Two Sets of Variates”, Biometrika 28, ss.321–377.

Hair, Jr., J. F., Black, W. C., Babin, B. J., Anderson, R. E., Tatham, R. L. (1998). Multivariate Data Analysis (5th Ed), Prentice Hall, New Jersey.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). “Measuring The Efficiency of Decision Making Units”, European Journal of Operation Research, 2,ss.429–444.

Pugh, R. C. ve Hu, Y. (1991), “Use and Interpretation of Canonical Correlation Analysis in Journal of Educational Research Articles: 1978-1989”, Journal of Educational Research, 84 (3), ss.147-152.

Adler, N. and J. Berechman, “Measuring airport quality from the airlines’ viewpoint: An application of data envelopment analysis,” Transport Policy, 8, 171–181 (2001).

Ali, A. I. and L. M. Seiford, “Translation invariance in data envelopment analysis,” Operations Research Letters, 9, 403–405 (1990).

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).