کاهش متغیرهای کلیدی ارزیابی بانک ها در بستر روش تحلیل پوششی داده ها با استفاده از مدلسازی معادلات ساختاری

Alirezaee, M. R., & Afsharian, M. (2007). A complete ranking of DMUs using restrictions in DEA models. Applied Mathematics and Computation, 189(2), 1550–1559. https://doi.org/10.1016/j.amc.2006.12.031

Amirteimoori, A., & Emrouznejad, A. (2012). Optimal input/output reduction in production processes. Decision Support Systems, 52(3), 742–747. https://doi.org/10.1016/j.dss.2011.11.020

Andersen, P., & Petersen, N. C. (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science, 39(10), 1261–1264. https://doi.org/10.1287/mnsc.39.10.1261

Banker, A. R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078

Benítez-Peña, S., Bogetoft, P., & Romero Morales, D. (2020). Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach. Omega (United Kingdom), 96. https://doi.org/10.1016/j.omega.2019.05.004

Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175–212. https://doi.org/10.1016/S0377-2217(96)00342-6

Bollen, K. A. (1989). Structural Equations with Latent Variables. John Wiley & Sons.

Carayannis, E. G., Grigoroudis, E., & Goletsis, Y. (2016). A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach. Expert Systems with Applications, 62, 63–80. https://doi.org/10.1016/j.eswa.2016.06.017

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Cooper, W. W., Sciford, L. M., & Tone, K. (2007). DATA ENVELOPMENT ANALYSIS A Comprehensive Text with Models , Applications , References Second Edition. Springer Science+Business Media.

David SHERMAN, H., & Gold, F. (1985). Evaluation with Data Envelopment Analysis. Journal of Banking and Finance, 9, 297–315.

Emrouznejad, A., & Yang, G. liang. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8. https://doi.org/10.1016/j.seps.2017.01.008

Eskelinen, J. (2016). Comparison of variable selection techniques for data envelopment analysis in a retail bank. European Journal of Operational Research, 259(2), 778–788. https://doi.org/10.1016/j.ejor.2016.11.009

Faramarzi, G. R., Khodakarami, M., Shabani, A., Farzipoor Saen, R., & Azad, F. (2015). New network data envelopment analysis approaches: An application in measuring sustainable operation of combined cycle power plants. Journal of Cleaner Production, 108. https://doi.org/10.1016/j.jclepro.2015.06.065

Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques : A survey. European Journal of Operational Research, 204(2), 189–198. https://doi.org/10.1016/j.ejor.2009.08.003

Friedman, L., & Sinuany-Stern, Z. (1998). Combining ranking scales and selecting variables in the DEA context: The case of industrial branches. Computers and Operations Research, 25(9), 781–791. https://doi.org/10.1016/S0305-0548(97)00102-0

Guo, C., Wei, F., Ding, T., Zhang, L., & Liang, L. (2017). Multistage network DEA: Decomposition and aggregation weights of component performance. Computers and Industrial Engineering, 113, 64–74. https://doi.org/10.1016/j.cie.2017.08.019

Henriques, I. C., Sobreiro, V. A., Kimura, H., & Mariano, E. B. (2018). Efficiency in the Brazilian banking system using data envelopment analysis. Future Business Journal, 4(2), 157–178. https://doi.org/10.1016/j.fbj.2018.05.001

Izadikhah, M., Tavana, M., & Di, D. (2017). A novel two-stage DEA production model with freely distributed initial inputs and shared intermediate outputs. Expert Systems With Applications, 0, 1–18. https://doi.org/10.1016/j.eswa.2017.11.005

Jenkins, L., & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147(1), 51–61. https://doi.org/10.1016/S0377-2217(02)00243-6

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. https://doi.org/10.1057/palgrave.jors.2601924

Li, Y., Shi, X., Yang, M., & Liang, L. (2017). Variable selection in data envelopment analysis via Akaike’s information criteria. Annals of Operations Research, 253(1), 453–476. https://doi.org/10.1007/s10479-016-2382-2

Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). A survey of DEA applications. Omega (United Kingdom), 41(5), 893–902. https://doi.org/10.1016/j.omega.2012.11.004

Luo, X. (2003). Evaluating the profitability and marketability efficiency of large banks: An application of data envelopment analysis. Journal of Business Research, 56(8), 627–635. https://doi.org/10.1016/S0148-2963(01)00293-4

Nataraja, N. R., & Johnson, A. L. (2011). Guidelines for using variable selection techniques in data envelopment analysis. European Journal of Operational Research, 215(3), 662–669. https://doi.org/10.1016/j.ejor.2011.06.045

Ohsato, S., & Takahashi, M. (2015). Management Efficiency in Japanese Regional Banks : A Network DEA. Procedia - Social and Behavioral Sciences, 172, 511–518. https://doi.org/10.1016/j.sbspro.2015.01.394

Paradi, J. C., Rouatt, S., & Zhu, H. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39(1), 99–109. https://doi.org/10.1016/j.omega.2010.04.002

Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega (United Kingdom), 41(1), 61–79. https://doi.org/10.1016/j.omega.2011.08.010

Paradi, J. C., Zhu, H., & Edelstein, B. (2012). Identifying managerial groups in a large Canadian bank branch network with a DEA approach. European Journal of Operational Research, 219(1), 178–187. https://doi.org/10.1016/j.ejor.2011.12.022

Pendharkar, P. C. (2020). A comparison of ensemble and dimensionality reduction dea models based on entropy criterion. Algorithms, 13(9). https://doi.org/10.3390/A13090232

Portela, M. C. A. S., Thanassoulis, E., & Simpson, G. (2004). Negative data in DEA: A directional distance approach applied to bank branches. Journal of the Operational Research Society, 55(10), 1111–1121. https://doi.org/10.1057/palgrave.jors.2601768

Raykov, T., & Marcoulides, G. A. M. (2006). A First Course in Structural Equation Modeling. In LAWRENCE ERLBAUM ASSOCIATES (second).

Subramanyam, T., Donthi, R., Satish Kumar, V., Amalanathan, S., & Zalki, M. (2021). A new stepwise method for selection of input and output variables in data envelopment analysis. Journal of Mathematical and Computational Science, 11(1), 703–715. https://doi.org/10.28919/jmcs/5205

Subramanyam T. (2016). Selection of Input-Output Variables in Data Envelopment Analysis -Indian Commercial Banks. International Journal of Computer & Mathematical Sciences IJCMS ISSN, 5(6), 2347–8527.

Toloo, M., Keshavarz, E., & Hatami-Marbini, A. (2021). Selecting data envelopment analysis models: A data-driven application to EU countries. Omega (United Kingdom), 101, 102248. https://doi.org/10.1016/j.omega.2020.102248

Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega (United Kingdom), 42(1), 124–131. https://doi.org/10.1016/j.omega.2013.04.002

Ueda, T., & Hoshiai, Y. (1997). Application of principal component analysis for parsimonious summarization of DEA inputs and/or outputs. Journal of the Operations Research Society of Japan, 40(4), 466–478.

Villanueva-Cantillo, J., & Munoz-Marquez, M. (2021). Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis. European Journal of Operational Research, 290(2), 657–670. https://doi.org/10.1016/j.ejor.2020.08.021

Wang, W. K., Lu, W. M., & Liu, P. Y. (2014). A fuzzy multi-objective two-stage DEA model for evaluating the performance of US bank holding companies. Expert Systems with Applications, 41(9), 4290–4297. https://doi.org/10.1016/j.eswa.2014.01.004

Wu, D. (Dash), Yang, Z., & Liang, L. (2006). Efficiency analysis of cross-region bank branches using fuzzy data envelopment analysis. Applied Mathematics and Computation, 181(1), 271–281. https://doi.org/10.1016/j.amc.2006.01.037

Yu, M., Lin, C., Chen, K., & Chen, L. (2019). Measuring Taiwanese bank performance: A two-system dynamic network data envelopment analysis approach. Omega, 102145. https://doi.org/10.1016/j.omega.2019.102145

Zarghami, S., & Amiri, M. (2021). A hybrid approach for performance evaluation and ranking of divisional structure organisations. International Journal Advanced Operations Management, 13(4), 431–458. https://doi.org/10.1504/IJAOM.2021.120780

Zohrehbandian, M., Makui, A., & Alinezhad, A. (2010). A compromise solution approach for finding common weights in DEA: An improvement to Kao and Hung’s approach. Journal of the Operational Research Society, 61(4), 604–610. https://doi.org/10.1057/ jors.2009.4