[1]Basso, A., & Funari, S. (2001). A data envelopment analysis approach to measure the mutual fund performance. European Journal of Operational Research, 135(3), 477-492.
[2]Mateus, I. B., Mateus, C., & Todorovic, N. (2019). Review of new trends in the literature on factor models and mutual fund performance. International Review of Financial Analysis, 63, 344-354.
[3]Cuthbertson, K., Nitzsche, D., & O’Sullivan, N. (2010). Mutual fund performance: Measurement and Evidence. Financial Markets, Institutions & Instruments, 19(2), 95-187.
[4]Prather, L., Bertin, W. J., & Henker, T. (2004). Mutual fund characteristics, managerial attributes, and fund performance. Review of Financial Economics, 13(4), 305-326.
[5]Elton, E. J., & Gruber, M. J. (2013). Mutual funds. Handbook of the Economics of Finance, 1011- 1061. Elsevier.
[6]Sharpe, W. F. (1966). Mutual fund performance. The Journal of business, 39(1), 119-138.
[7]Vidal-García, J., Vidal, M., Boubaker, S., & Hassan, M. (2018). The efficiency of mutual funds. Annals of Operations Research, 267, 555-584.
[8]Basso, A., & Funari, S. (2016). DEA performance assessment of mutual funds. Data Envelopment Analysis: A Handbook of Empirical Studies and Applications, 229-287. Springer, Boston, MA.
[9]Daniel, D., & John, R. (2024). Assessing mutual fund success: A comprehensive literature review of efficiency measurement through data envelopment analysis (DEA). Ajasra, 13(2), 239-255.
[10]Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
[11]Banker, 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.
[12]Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on Data Envelopment Analysis. Springer, New York, NY.
[13]Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). A survey of DEA applications. Omega, 41(5), 893-902.
[14]Emrouznejad, A., & Yang, G. L. (2018). A survey and Analysis of the First 40 Years of Scholarly Literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8.
[15]Premachandra, I. M., Zhu, J., Watson, J., & Galagedera, D. U. A. (2012). Best-performing US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition. Journal of Banking & Finance, 36(12), 3302–3317.
[16]Galagedera, D. U. A., Watson, J., Premachandra, I. M., & Chen, Y. (2016). Modeling leakage in two-stage DEA models: An application to US mutual fund families. Omega, 61, 62–77.
[17]Premachandra, I. M., Zhu, J., Watson, J., & Galagedera, D. U. A. (2016). Mutual fund industry performance: a network data envelopment analysis approach. Data Envelopment Analysis, 165-228. Springer, Boston, MA.
[18]Sánchez-González, C., Sarto, J. L., & Vicente, L. (2017). The efficiency of mutual fund companies: evidence from an innovative network SBM approach. Omega, 71, 114-128.
[19]Galagedera, D. U. A., Roshdi, I., Fukuyama, H., & Zhu, J. (2018). A new network DEA model for mutual fund performance appraisal: An application to U.S. equity mutual funds. Omega, 77, 168–179.
[20]Galagedera, D. U. A. (2019). Modelling social responsibility in mutual fund performance appraisal: A two-stage data envelopment analysis model with non-discretionary first stage output. European Journal of Operational Research, 273(1), 376–389.
[21]Hsieh, H. P., Tebourbi, I., Lu, W. M., & Liu, N. Y. (2020). Mutual fund performance: the decision quality and capital magnet efficiencies. Managerial and Decision Economics, 41(5), 861-872.
[22]Galagedera, D. U. A., Fukuyama, H., Watson, J., & Tan, E. K. (2020). Do mutual fund managers earn their fees? new measures for performance appraisal. European Journal of Operational Research, 287(2), 653-667.
[23]Tsolas, I. E. (2020). Precious metal mutual fund performance evaluation: a series two-stage DEA modeling approach. Journal of Risk and Financial Management, 13(5), 87.
[24]Fukuyama, H., & Galagedera, D. U. A. (2021). Value extracting in relative performance appraisal with network DEA: An application to US equity mutual funds. Data-Enabled Analytics: DEA for Big Data, 263-297. Cham: Springer International Publishing.
[25]Peykani, P., Emrouznejad, A., Mohammadi, E., & Gheidar-Kheljani, J. (2024). A novel robust net- work data envelopment analysis approach for performance assessment of mutual funds under uncertainty. Annals of Operations Research, 339(3), 1149-1175.
[26]Shojaie, S. E., Sadjadi, S. J., & Tavakkoli-Moghaddam, R. (2024). Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study. Plos One, 19(7), e0307277.
[27]Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196(3), 1170-1176.
[28]Castelli, L., Pesenti, R., & Ukovich, W. (2010). A Classification of DEA Models When the Internal Structure of the Decision Making Units is Considered. Annals of Operations Research, 173(1), 207- 235.
[29]Cook, W. D., & Zhu, J. (2014). Data Envelopment Analysis: A Handbook of Modeling Internal Structure and Network. Springer.
[30]Kao, C. (2014). Network data envelopment analysis: A review. European Journal of Operational Research, 239(1), 1-16.
[31]Kao, C. (2016). Network Data Envelopment Analysis: Foundations and Extensions. Springer.
[32]Koronakos, G. (2019). A taxonomy and review of the network data envelopment analysis literature. Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems, 255- 311.
[33]Peykani, P., Mohammadi, E., & Emrouznejad, A. (2021). An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms. Expert Systems with Applications, 166, 113938.
[34]Ratner, S. V., Shaposhnikov, A. M., & Lychev, A. V. (2023). Network DEA and its applications (2017–2022): A systematic literature review. Mathematics, 11(9), 2141.
[35]Peykani, P., Seyed Esmaeili, F. S., Pishvaee, M. S., Rostamy-Malkhalifeh, M., & Hosseinzadeh Lotfi, F. (2024). Matrix-based network data envelopment analysis: A common set of weights approach. Socio-Economic Planning Sciences, 95, 102044.
[36]Charnes, A., & Cooper, W. W. (1962). Programming with Linear Fractional Functionals. Naval Research Logistics Quarterly, 9(3‐4), 181-186.
[37]Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429.
[38]Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M. S., & Rostamy-Malkhalifeh, M. (2019). Fuzzy data envelopment analysis: an adjustable approach. Expert Systems with Applica- tions, 136, 439-452.
[39]Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3-28.
[40]Inuiguchi, M., & Ramı́k, J. (2000). Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3-28.
[41]Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management Science, 6(1), 73-79.
[42]Peykani, P., Hosseinzadeh Lotfi, F., Sadjadi, S. J., Ebrahimnejad, A., & Mohammadi, E. (2022). Fuzzy chance-constrained data envelopment analysis: a structured literature review, current trends, and future directions. Fuzzy Optimization and Decision Making, 21(2), 197–261.
[43]Xu, J., & Zhou, X. (2011). Fuzzy-Like Multiple Objective Decision Making. Berlin: Springer.
[44]Xu, J., & Zhou, X. (2013). Approximation based fuzzy multi-objective models with expected objectives and chance constraints: application to earth-rock work allocation. Information Sciences, 238, 75-95.
[45]Peykani, P., Mohammadi, E., Pishvaee, M. S., Rostamy-Malkhalifeh, M., & Jabbarzadeh, A. (2018). A novel fuzzy data envelopment analysis based on robust possibilistic programming: Possibility, necessity and credibility-based approaches. RAIRO-Operations Research, 52(4-5), 1445–1463.
[46]An, Q., Meng, F., Xiong, B., Wang, Z., & Chen, X. (2020). Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach. Annals of Operations Research, 290, 707-729.
[47]Peykani, P., Farzipoor Saen, R., Seyed Esmaeili, F. S., & Gheidar‐Kheljani, J. (2021). Window data envelopment analysis approach: A review and bibliometric analysis. Expert Systems, 38(7), e12721.
[48]Yu, A., Shi, Y., You, J., & Zhu, J. (2021). Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach. European Journal of Operational Research, 292(1), 199-212.
[49]Fukuyama, H., Tsionas, M., & Tan, Y. (2023). Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: application to the Chinese banking industry. European Journal of Operational Research, 307(3), 1360-1373.
[50]Tavana, M., Khalili-Damghani, K., Santos Arteaga, F. J., & Hashemi, A. (2020). A Malmquist pro- ductivity index for network production systems in the energy sector. Annals of Operations Research, 284, 415-445.
[51]Shakouri, R., Salahi, M., & Kordrostami, S. (2019). Stochastic p-robust approach to two-stage net- work DEA model. Quantitative Finance and Economics, 3(2), 315-346.
[52]Peykani, P., Mohammadi, E., Farzipoor Saen, R., Sadjadi, S. J., & Rostamy‐Malkhalifeh, M. (2020). Data envelopment analysis and robust optimization: A review. Expert Systems, 37(4), e12534.
[53]Salahi, M., Toloo, M., & Torabi, N. (2021). A new robust optimization approach to common weights formulation in DEA. Journal of the Operational Research Society, 72(6), 1390-1402.
[54]Peykani, P., Gheidar-Kheljani, J., Farzipoor Saen, R., & Mohammadi, E. (2022). Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data. Operational Research, 22(5), 5529-5567.
[55]Ghaffari-Hadigheh, A., & Lio, W. (2020). Network data envelopment analysis in uncertain environment. Computers & Industrial Engineering, 148, 106657.
[56]Jiang, B., Chen, H., Li, J., & Lio, W. (2021). The uncertain two-stage network DEA models. Soft Computing, 25, 421-429.
[57]Jiang, B., Yang, C., & Li, J. (2021). The uncertain network DEA model for two-stage system with additive relationship. Symmetry, 13(10), 1893.
[58]Peykani, P., & Pishvaee, M. S. (2024). Performance evaluation of hospitals under data uncertainty: An uncertain common-weights data envelopment analysis. Healthcare, 12(6), 611.
[59]Zadeh, L. A. (2011). A note on Z-numbers. Information Sciences, 181(14), 2923-2932.
[60]Azadeh, A., & Kokabi, R. (2016). Z-number DEA: A new possibilistic DEA in the context of Z- numbers. Advanced Engineering Informatics, 30(3), 604-617.
[61]Nazari-Shirkouhi, S., Tavakoli, M., Govindan, K., & Mousakhani, S. (2023). A hybrid approach using Z-number DEA model and artificial neural network for resilient supplier selection. Expert Systems with Applications, 222, 119746.
[62]Seyed Esmaeili, F. S., & Mohammadi, E. (2024). Z-number network data envelopment analysis approach: A case study on the Iranian insurance industry. Plos One, 19(7), e0306876.
