[
Anderson, T.W. (1962). On the distribution of the two-sample Cramer–von Mises criterion, The Annals of Mathematical Statistics 33(3): 1148–1159.
]Search in Google Scholar
[
Ban, A., Coroianu, L. and Grzegorzewski, P. (2015). Fuzzy Numbers: Approximations, Ranking and Applications, Polish Academy of Sciences, Warsaw.
]Search in Google Scholar
[
Chernick, M.R., González-Manteiga, W., Crujeiras, R.M. and Barrios, E.B. (2011). Bootstrap methods, in M. Lovric (Ed.), International Encyclopedia of Statistical Science, Springer, Berlin/Heidelberg, pp. 169–174.
]Search in Google Scholar
[
Couso, I. and Dubois, D. (2014). Statistical reasoning with set-valued information: Ontic vs. epistemic views, International Journal of Approximate Reasoning 55(7): 1502–1518.
]Search in Google Scholar
[
De Angelis, D. and Young, G.A. (1992). Smoothing the bootstrap, International Statistical Review 60(1): 45–56.
]Search in Google Scholar
[
Efron, B. (1979). Bootstrap methods: Another look at the jackknife, Annals of Statistics 7(1): 1–26.
]Search in Google Scholar
[
Faraz, A. and Shapiro, A.F. (2010). An application of fuzzy random variables to control charts, Fuzzy Sets and Systems 161(20): 2684–2694.
]Search in Google Scholar
[
Gibbons, J.D. and Chakraborti, S. (2010). Nonparametric Statistical Inference, Chapman and Hall/CRC, New York.
]Search in Google Scholar
[
Gil, M.A., Lubiano, M.A., Montenegro, M. and López, M.T. (2002). Least squares fitting of an affine function and strength of association for interval-valued data, Metrika 56(2): 97–111.
]Search in Google Scholar
[
Gil, M., Montenegro, M., González-Rodríguez, G., Colubi, A. and Casals, M. (2006). Bootstrap approach to the multi-sample test of means with imprecise data, Computational Statistics and Data Analysis 51(1): 148–162.
]Search in Google Scholar
[
González-Rodríguez, G., Montenegro, M., Colubi, A. and Gil, M. (2006). Bootstrap techniques and fuzzy random variables: Synergy in hypothesis testing with fuzzy data, Fuzzy Sets and Systems 157(19): 2608–2613.
]Search in Google Scholar
[
Grzegorzewski, P. (2008). Trapezoidal approximations of fuzzy numbers preserving the expected interval—Algorithms and properties, Fuzzy Sets and Systems 159(11): 1354–1364.
]Search in Google Scholar
[
Grzegorzewski, P. (2020). Permutation k-sample goodness-of-fit test for fuzzy data, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, pp. 1–8.
]Search in Google Scholar
[
Grzegorzewski, P. and Gadomska, O. (2021). Nearest neighbor tests for fuzzy data, 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, pp. 1–6.
]Search in Google Scholar
[
Grzegorzewski, P., Hryniewicz, O. and Romaniuk, M. (2019). Flexible bootstrap based on the canonical representation of fuzzy numbers, Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), Prague, Czech Republic, pp. 490–497.
]Search in Google Scholar
[
Grzegorzewski, P., Hryniewicz, O. and Romaniuk, M. (2020a). Flexible bootstrap for fuzzy data based on the canonical representation, International Journal of Computational Intelligence Systems 13(1): 1650–1662.
]Search in Google Scholar
[
Grzegorzewski, P., Hryniewicz, O. and Romaniuk, M. (2020b). Flexible resampling for fuzzy data, International Journal of Applied Mathematics and Computer Science 30(2): 281–297, DOI: 10.34768/amcs-2020-0022.
]Search in Google Scholar
[
Grzegorzewski, P. and Romaniuk, M. (2021). Epistemic bootstrap for fuzzy data, Joint Proceedings of the IFSAEUSFLAT- AGOP 2021 Conferences, Bratislavia, Slovakia, pp. 538–545.
]Search in Google Scholar
[
Grzegorzewski, P. and Romaniuk, M. (2022a). Bootstrap methods for epistemic fuzzy data, International Journal of Applied Mathematics and Computer Science 32(2): 285–297, DOI: 10.34768/amcs-2022-0021.
]Search in Google Scholar
[
Grzegorzewski, P. and Romaniuk, M. (2022b). Bootstrapped Kolmogorov–Smirnov test for epistemic fuzzy data, in D. Ciucci et al. (Eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer International Publishing, Cham, pp. 494–507.
]Search in Google Scholar
[
Hesamian, G., Akbari, M.G. and Shams, M. (2023). A goodness-of-fit test based on fuzzy random variables, Fuzzy Information and Engineering 15(1): 55–68.
]Search in Google Scholar
[
Hesamian, G. and Taheri, S. (2013). Linear rank tests for two-sample fuzzy data: A p-value approach, Journal of Uncertain Systems 7(2): 129–137.
]Search in Google Scholar
[
Kruse, R. (1982). The strong law of large numbers for fuzzy random variables, Information Sciences 28(3): 233–241.
]Search in Google Scholar
[
Kwakernaak, H. (1978). Fuzzy random variables. Part I: Definitions and theorems, Information Sciences 15(1): 1–15.
]Search in Google Scholar
[
Lubiano, M.A., Salas, A., Carleos, C., de la Rosa de Sáa, S. and Gil, M.A. (2017). Hypothesis testing-based comparative analysis between rating scales for intrinsically imprecise data, International Journal of Approximate Reasoning 88: 128–147.
]Search in Google Scholar
[
Lun, A. (2021). metapod: Meta-Analyses on p-Values of Differential Analyses, R package, http://www.bioconductor.org/packages/release/bioc/html/metapod.html.
]Search in Google Scholar
[
Montenegro, M., Colubi, A., Casals, M. and Gil, M. (2004). Asymptotic and bootstrap techniques for testing the expected value of a fuzzy random variable, Metrika 59: 31–49.
]Search in Google Scholar
[
Romaniuk, M. and Grzegorzewski, P. (2023). Resampling fuzzy numbers with statistical applications: FuzzyResampling package, The R Journal 15(1): 271–283.
]Search in Google Scholar
[
Romaniuk, M., Grzegorzewski, P. and Parchami, A. (2023). FuzzySimRes: Simulation and Resampling Methods for Epistemic Fuzzy Data, R package, Version 0.2.0, https://CRAN.R-project.org/package=FuzzySimRes.
]Search in Google Scholar
[
Romaniuk, M. and Hryniewicz, O. (2021). Discrete and smoothed resampling methods for interval-valued fuzzy numbers, IEEE Transactions on Fuzzy Systems 29(3): 599–611.
]Search in Google Scholar
[
Simes, R.J. (1986). An improved Bonferroni procedure for multiple tests of significance, Biometrika 73(3): 751–754.
]Search in Google Scholar
[
Smirnov, N. (1933). Estimate of deviation between empirical distribution functions in two independent samples, Bulletin of Moscow University 2: 3–16.
]Search in Google Scholar
[
Trutschnig, W., González-Rodríguez, G., Colubi, A. and Gil, M.A. (2009). A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spread, Information Sciences 179(23): 3964–3972.
]Search in Google Scholar
[
Vovk, V. and Wang, R. (2020). Combining p-values via averaging, Biometrika 107(4): 791–808.
]Search in Google Scholar
[
Xiao, Y. (2012). CvM2SL1Test: L1-Version of Cramer–von Mises Two Sample Tests, R package, https://github.com/cran/CvM2SL1Test.
]Search in Google Scholar