Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European journal of operational research, 140(2), 249-265.
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264.
Alp, Ä°. (2016). Another way to determine weights of balanced performance evaluations. Dumlupinar University Journal of Social Science, (Special Issue of ICEBSS), 151-161.
Angulo-Meza, L., & Lins, M. P. E. (2002). Review of methods for increasing discrimination in data envelopment analysis. Annals of Operations Research, 116(1-4), 225-242.
Bal, H., & Örkcü, H. H. (2011). A new mathematical programming approach to multi-group classification problems. Computers & Operations Research, 38(1), 105-111.
Casella, G. (2003). Introduction to the Silver Anniversary of the Bootstrap. Statistical Science, 18(2), 133-134.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Chernick, M. R. (2008). Bootstrap methods: a guide for practitioners and researchers. New York. ISBN 978-0-471-75621-7.
Cook, W. D., & Zhu, J. (2007). Within-group common weights in DEA: An analysis of power plant efficiency. European Journal of Operational Research, 178(1), 207-216.
Cooper, W. W., & Tone, K. (1997). Measures of inefficiency in data envelopment analysis and stochastic frontier estimation. European Journal of Operational Research, 99(1), 72-88.
Daniel, W. W. (1990). Applied nonparametric statistics. Boston: Duxbury/Thomson Learning.
Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the operational research society, 45(5), 567-578.
Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Society for industrial and applied mathematics
[H1] , Philadelphia, PA.
Efron, B., Tibshirani, R.J., (1993). An Introduction to the Bootstrap. London: Chapman and Hall.
Fisher RA. (1935). The Design of Experiments. New York: Hafner.
Friedman, L., & Sinuany-Stern, Z. (1997). Scaling units via the canonical correlation analysis in the DEA context. European Journal of Operational Research, 100(3), 629-637.
Ganley, J. A., & Cubbin, J. S. (1992). Public sector efficiency measurement: Applications of data envelopment analysis. North-Holland, Amsterdam
[H2] , Elsevier Science Publishers.
Kao, C., & Hung, H. T. (2005). Data envelopment analysis with common weights: the compromise solution approach. Journal of the Operational Research Society, 56(10), 1196-1203.
Lam, K. F., & Bai, F. (2011). Minimizing deviations of input and output weights from their means in data envelopment analysis. Computers & Industrial Engineering, 60(4), 527-533.
Li, X. B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517.
Lin, R., Chen, Z., & Xiong, W. (2016). An iterative method for determining weights in cross efficiency evaluation. Computers & Industrial Engineering, 101, 91-102.
Liu, F. H. F., & Peng, H. H. (2008). Ranking of units on the DEA frontier with common weights. Computers & Operations Research, 35(5), 1624-1637.
Makui, A., Alinezhad, A., Kiani Mavi, R., & Zohrehbandian, M. (2008). A goal programming method for finding common weights in DEA with an improved discriminating power for efficiency. Journal of Industrial and Systems Engineering, 1(4), 293-303.
Mecit, E. D., & Alp, I. (2013). A new proposed model of restricted data envelopment analysis by correlation coefficients. Applied Mathematical Modelling, 37(5), 3407-3425.
Pigeot, I. (2001). The jackknife and bootstrap in biomedical research—common principles and possible pitfalls. Drug information journal, 35(4), 1431-1443.
Pitman, E. J. (1937). Significance tests which may be applied to samples from any populations. Supplement to the Journal of the Royal Statistical Society, 4(1), 119-130.
Pitman, E. J. G. (1938). Significance tests which may be applied to samples from any populations: III. The analysis of variance test. Biometrika, 29(3/4), 322-335.
Podinovski, V. V., & Thanassoulis, E. (2007). Improving discrimination in data envelopment analysis: Some practical suggestions. Journal of Productivity Analysis, 28(1-2), 117-126.
Quenouille, M. H. (1949). Approximate tests of correlation in time-series. Journal Of The Royal Statistical Society Series B-Statistical Methodology, 11(1), 68-84.
Roll, Y., Cook, W. D., & Golany, B. (1991). Controlling factor weights in data envelopment analysis. IIE transactions, 23(1), 2-9.
Shang, J., & Sueyoshi, T. (1995). A unified framework for the selection of a flexible manufacturing system. European Journal of Operational Research, 85(2), 297-315.
Sinuany-Stern, Z., & Friedman, L. (1998). DEA and the discriminant analysis of ratios for ranking units. European Journal of Operational Research, 111(3), 470-478.
Sinuany-Stern, Z., Mehrez, A., & Barboy, A. (1994). Academic departments efficiency via DEA. Computers & Operations Research, 21(5), 543-556.
Thompson, R. G., Langemeier, L. N., Lee, C. T., Lee, E., & Thrall, R. M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of econometrics, 46(1-2), 93-108.
Torgersen, A. M., Førsund, F. R., & Kittelsen, S. A. (1996). Slack-adjusted efficiency measures and ranking of efficient units. Journal of Productivity Analysis, 7(4), 379-398.
Troutt, M. D. (1995). A maximum decisional efficiency estimation principle. Management Science, 41(1), 76-82.
Wong, Y. H., & Beasley, J. E. (1990). Restricting weight flexibility in data envelopment analysis. Journal of the Operational Research Society, 41(9), 829-835.
[H2]Before publisher's name